Udacity - Data Scientist Nanodegree nd025 v1.0.0

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.tr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.zh-Hans.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.it.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.pt-PT.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.it.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-o91iPvtqt78.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.hr.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.it.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.zh-Hans.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-8j5hria6Rc8.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-f3y_weFskL4.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.zh-Hans.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.th.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2--pOzdj6pnbA.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-rDw0TIpwJ-c.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.es-ES.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ja.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.zh-Hans.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.es-ES.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.it.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.it.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.es-ES.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.es-ES.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.th.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.ja.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.ar.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-dyShKWpTo-c.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-etrUbOAoh1U.th.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-tIrqdYTT_9Q.th.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.ja.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-dyShKWpTo-c.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.es-ES.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.it.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.es-ES.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.ja.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.ja.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.es-ES.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.es-ES.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-jO81hfubpXY.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-G9yQ_URDrDQ.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.hr.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.en.vtt

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Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.hr.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.zh-CN.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-jO81hfubpXY.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.it.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-ztkKTrMZHXg.th.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.ja.vtt

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Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.hr.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.es-ES.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-jO81hfubpXY.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.ja.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.ja.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.hr.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-yo55zJtJQwo.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.ja.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-4qW7a5E74No.th.vtt

160 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.es-ES.vtt

160 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.zh-CN.vtt

160 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.en.vtt

161 B

Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.ar.vtt

161 B

Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.pt-BR.vtt

162 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.zh-CN.vtt

163 B

Part 12-Module 01-Lesson 07_Bayes Rule/12. Normalizer-W5i-gRAvZxs.ar.vtt

163 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.hr.vtt

163 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.it.vtt

164 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.pt-BR.vtt

164 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.zh-CN.vtt

164 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.pt-BR.vtt

164 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-E1ph6NP3_v4.th.vtt

164 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.en.vtt

165 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.es-ES.vtt

165 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.es-ES.vtt

166 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.ar.vtt

166 B

Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.ar.vtt

167 B

Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.pt-BR.vtt

167 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.en.vtt

167 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-udXhxyls5Dw.pt-BR.vtt

168 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.pt-BR.vtt

169 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.pt-BR.vtt

169 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.en.vtt

169 B

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.pt-BR.vtt

170 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.es-ES.vtt

170 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.it.vtt

171 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.it.vtt

171 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LNzmJUj8K8w.pt-BR.vtt

171 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.pt-BR.vtt

173 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.pt-BR.vtt

173 B

Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.it.vtt

173 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.pt-BR.vtt

173 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.it.vtt

174 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.es-ES.vtt

174 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.es-ES.vtt

174 B

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.en.vtt

174 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.ja.vtt

175 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.ar.vtt

175 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.ar.vtt

176 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.ja.vtt

176 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.pt-BR.vtt

177 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.th.vtt

177 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.zh-CN.vtt

177 B

Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.en.vtt

178 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.th.vtt

178 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.it.vtt

178 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-CN.vtt

180 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.zh-CN.vtt

180 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.en.vtt

180 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ja.vtt

180 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.zh-CN.vtt

182 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.en.vtt

182 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.pt-BR.vtt

183 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.pt-BR.vtt

184 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.ar.vtt

184 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-GsneDVJB75E.ja.vtt

184 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.es-ES.vtt

185 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.pt-BR.vtt

186 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.zh-CN.vtt

186 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.en.vtt

186 B

Part 12-Module 01-Lesson 03_Admissions Case Study/08. Aggregation 2-xhpEqsHTf3g.it.vtt

187 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.zh-CN.vtt

187 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ja.vtt

187 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.pt-BR.vtt

188 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.zh-Hans.vtt

189 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.ar.vtt

190 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.ja.vtt

190 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.en.vtt

190 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.ar.vtt

191 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.zh-CN.vtt

192 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.es-ES.vtt

192 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.ja.vtt

194 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.ja.vtt

194 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.en.vtt

196 B

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces-jkjr_prWyt8.ar.vtt

196 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.zh-CN.vtt

196 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ja.vtt

197 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.zh-CN.vtt

197 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.hr.vtt

197 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.zh-CN.vtt

198 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ja.vtt

198 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.en.vtt

198 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.ja.vtt

199 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ja.vtt

200 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.ja.vtt

201 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.pt-BR.vtt

201 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.ar.vtt

202 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ja.vtt

202 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.pt-BR.vtt

202 B

Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.pt-BR.vtt

203 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.hr.vtt

204 B

Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4--GMhV1twy6Y.ar.vtt

204 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-fqt7NIvMB0s.th.vtt

204 B

Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.zh-CN.vtt

204 B

Part 12-Module 01-Lesson 07_Bayes Rule/17. Disease Test 4-UERKMwmkAsM.th.vtt

205 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.it.vtt

205 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.ar.vtt

207 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.zh-CN.vtt

207 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ar.vtt

208 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.hr.vtt

209 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.pt-BR.vtt

209 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.en.vtt

210 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.it.vtt

210 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.zh-CN.vtt

210 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.es-ES.vtt

210 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.ar.vtt

211 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.pt-BR.vtt

212 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.zh-CN.vtt

212 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ja.vtt

212 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.it.vtt

212 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.zh-CN.vtt

212 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.hr.vtt

213 B

Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.en.vtt

213 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.it.vtt

213 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.en.vtt

214 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.zh-CN.vtt

214 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.en.vtt

214 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.en.vtt

214 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.pt-PT.vtt

215 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.zh-CN.vtt

215 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-7ZLe_JP5wRY.pt-BR.vtt

216 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ja.vtt

216 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.hr.vtt

216 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.zh-CN.vtt

216 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.en.vtt

216 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.en.vtt

217 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ja.vtt

217 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.es-ES.vtt

217 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.zh-CN.vtt

218 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-0bEbJ33dUis.ar.vtt

218 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.pt-BR.vtt

218 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-Nn_8kCRYn2k.ar.vtt

218 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.es-ES.vtt

219 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.ja.vtt

219 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.pt-BR.vtt

219 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.en.vtt

219 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.en.vtt

219 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.es-ES.vtt

221 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.ar.vtt

222 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ar.vtt

222 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.en.vtt

222 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.ar.vtt

222 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.pt-BR.vtt

223 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.pt-BR.vtt

223 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.zh-CN.vtt

225 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.zh-CN.vtt

226 B

Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.es-ES.vtt

227 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.zh-CN.vtt

228 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.th.vtt

228 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.zh-CN.vtt

228 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.en.vtt

231 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.es-ES.vtt

231 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.it.vtt

231 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.zh-CN.vtt

232 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.es-ES.vtt

233 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.it.vtt

234 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.es-ES.vtt

234 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.ar.vtt

234 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.es-ES.vtt

234 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ja.vtt

236 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.es-ES.vtt

236 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ja.vtt

237 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.ar.vtt

238 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.hr.vtt

238 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.en.vtt

238 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.es-ES.vtt

238 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.it.vtt

239 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.es-ES.vtt

239 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-Se-ddM2Wdac.th.vtt

240 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.it.vtt

240 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.en.vtt

240 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.pt-BR.vtt

240 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-_hXCgF-aMB0.th.vtt

240 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.zh-CN.vtt

241 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ja.vtt

241 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.en.vtt

241 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.zh-CN.vtt

241 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.es-ES.vtt

242 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.es-ES.vtt

243 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.ja.vtt

243 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.zh-CN.vtt

244 B

Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-goEMc0w58xM.th.vtt

244 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.en.vtt

244 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.en.vtt

245 B

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.en.vtt

246 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.en.vtt

246 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.pt-BR.vtt

246 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.en.vtt

247 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.ja.vtt

247 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ja.vtt

247 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.en.vtt

248 B

Part 12-Module 01-Lesson 16_Logistic Regression/16. How Many Schroeders-T2dveKB64Ho.ar.vtt

248 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.zh-CN.vtt

248 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.ja.vtt

248 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.it.vtt

248 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.pt-BR.vtt

249 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.pt-BR.vtt

249 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.pt-BR.vtt

249 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.pt-BR.vtt

249 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.ar.vtt

250 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.zh-CN.vtt

250 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.es-ES.vtt

251 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.zh-CN.vtt

251 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.es-ES.vtt

252 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.en.vtt

252 B

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-d_fbDqAGVdE.ar.vtt

252 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.hr.vtt

252 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.th.vtt

252 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.pt-BR.vtt

253 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ja.vtt

254 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.ar.vtt

254 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.pt-BR.vtt

254 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.zh-CN.vtt

256 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.en.vtt

256 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.en.vtt

256 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.es-ES.vtt

257 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.zh-CN.vtt

257 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.es-ES.vtt

258 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.en.vtt

260 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.es-ES.vtt

260 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ja.vtt

260 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.pt-BR.vtt

260 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ja.vtt

261 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ja.vtt

262 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.pt-BR.vtt

262 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ja.vtt

264 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.pt-BR.vtt

264 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-d4LWnxyvrTQ.ar.vtt

265 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-sNvQeSikRFY.ar.vtt

265 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.pt-BR.vtt

265 B

Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.ar.vtt

265 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.th.vtt

267 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.en.vtt

268 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.zh-CN.vtt

268 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.pt-BR.vtt

269 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.es-ES.vtt

272 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.zh-CN.vtt

275 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.pt-BR.vtt

276 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.zh-CN.vtt

277 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.en.vtt

277 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.ar.vtt

278 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.ar.vtt

279 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.en.vtt

279 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.zh-CN.vtt

280 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.es-ES.vtt

281 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.pt-BR.vtt

281 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.en.vtt

281 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.zh-CN.vtt

282 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.ar.vtt

283 B

Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.zh-CN.vtt

283 B

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.pt-BR.vtt

284 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.pt-BR.vtt

285 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.ar.vtt

286 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.zh-CN.vtt

288 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.hr.vtt

289 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.it.vtt

289 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.zh-CN.vtt

291 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.pt-BR.vtt

291 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.it.vtt

291 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.hr.vtt

291 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.en.vtt

292 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ja.vtt

293 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.pt-BR.vtt

293 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.zh-CN.vtt

293 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.zh-CN.vtt

294 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.zh-CN.vtt

296 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ja.vtt

296 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.zh-CN.vtt

297 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.zh-CN.vtt

298 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ja.vtt

298 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.th.vtt

299 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ja.vtt

300 B

Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.ja.vtt

300 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ja.vtt

301 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.es-ES.vtt

301 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ja.vtt

301 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.zh-CN.vtt

302 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ja.vtt

302 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.ar.vtt

302 B

Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.en.vtt

302 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.ar.vtt

302 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.zh-CN.vtt

303 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.zh-CN.vtt

303 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-yof0QiP2mzk.ar.vtt

303 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.ar.vtt

303 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ja.vtt

304 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.zh-CN.vtt

304 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ja.vtt

304 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.pt-BR.vtt

305 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.zh-CN.vtt

305 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ja.vtt

305 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.hr.vtt

305 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6-iyE5h48qPFQ.th.vtt

305 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ja.vtt

305 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.en.vtt

306 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.it.vtt

307 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.pt-BR.vtt

307 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.en.vtt

307 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.zh-CN.vtt

307 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.it.vtt

308 B

Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.pt-BR.vtt

309 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.zh-CN.vtt

309 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.en.vtt

310 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ja.vtt

310 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.it.vtt

311 B

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-4YY-hmqSz30.ar.vtt

312 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.hr.vtt

312 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.en.vtt

313 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.zh-CN.vtt

314 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.it.vtt

314 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.it.vtt

315 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ja.vtt

315 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.ar.vtt

315 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.zh-CN.vtt

316 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.pt-BR.vtt

317 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.en.vtt

317 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.es-ES.vtt

318 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.en.vtt

319 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-r8stm2et_hI.ar.vtt

319 B

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.ar.vtt

319 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.hr.vtt

319 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.zh-CN.vtt

320 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.en.vtt

322 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.zh-CN.vtt

323 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ja.vtt

323 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.it.vtt

324 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.ar.vtt

325 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.en.vtt

325 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.zh-CN.vtt

325 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ja.vtt

326 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.ar.vtt

326 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ja.vtt

326 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ja.vtt

327 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.es-ES.vtt

327 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.es-ES.vtt

329 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.pt-BR.vtt

329 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.zh-CN.vtt

331 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ja.vtt

331 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ja.vtt

332 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.es-ES.vtt

332 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.pt-BR.vtt

332 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.es-ES.vtt

333 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.zh-CN.vtt

333 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.ar.vtt

334 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.pt-BR.vtt

335 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.zh-CN.vtt

335 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.ar.vtt

336 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.en.vtt

336 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.es-ES.vtt

337 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.en.vtt

337 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.pt-BR.vtt

337 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.it.vtt

338 B

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.ar.vtt

339 B

Part 12-Module 01-Lesson 07_Bayes Rule/06. Normalizing 3-V96RcbbVP7Q.th.vtt

339 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.en.vtt

339 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.pt-BR.vtt

339 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.en.vtt

340 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.zh-CN.vtt

341 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.en.vtt

341 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.it.vtt

341 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.ar.vtt

341 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.th.vtt

342 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.en.vtt

342 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.en.vtt

342 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.ar.vtt

342 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.en.vtt

343 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.pt-BR.vtt

344 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ja.vtt

345 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.en.vtt

346 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.es-ES.vtt

348 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.es-ES.vtt

348 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.zh-CN.vtt

348 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.en.vtt

349 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.en.vtt

349 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.pt-BR.vtt

349 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.en.vtt

350 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ja.vtt

350 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.es-ES.vtt

350 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.en.vtt

351 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.zh-CN.vtt

351 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.zh-CN.vtt

352 B

Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bEAaNv-CBQ4.ar.vtt

352 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.en.vtt

353 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ja.vtt

353 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.en.vtt

354 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.pt-BR.vtt

354 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.th.vtt

354 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.pt-BR.vtt

354 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.zh-CN.vtt

355 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.es-ES.vtt

355 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.zh-CN.vtt

355 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.es-ES.vtt

356 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.pt-BR.vtt

356 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ja.vtt

357 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.es-ES.vtt

357 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.en.vtt

357 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.es-ES.vtt

357 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt

357 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.es-ES.vtt

358 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.en.vtt

358 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.it.vtt

359 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-uimwo-puQWY.th.vtt

359 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.es-ES.vtt

360 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.pt-BR.vtt

360 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.pt-BR.vtt

362 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.zh-CN.vtt

362 B

Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.ar.vtt

364 B

Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt

364 B

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt

364 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt

364 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.en.vtt

364 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.es-ES.vtt

366 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.zh-CN.vtt

366 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.es-ES.vtt

367 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.zh-CN.vtt

369 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.zh-CN.vtt

369 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.en.vtt

370 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ja.vtt

370 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ja.vtt

371 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt

371 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.it.vtt

372 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.pt-BR.vtt

373 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ja.vtt

373 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.en.vtt

373 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ja.vtt

374 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.it.vtt

375 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.pt-BR.vtt

375 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.zh-CN.vtt

376 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.pt-BR.vtt

376 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.pt-BR.vtt

377 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.pt-BR.vtt

377 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.zh-CN.vtt

378 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.zh-CN.vtt

379 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.zh-CN.vtt

380 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.zh-CN.vtt

380 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.en.vtt

382 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.it.vtt

382 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.hr.vtt

383 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.pt-BR.vtt

383 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.hr.vtt

384 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.en.vtt

387 B

Part 12-Module 01-Lesson 16_Logistic Regression/11. Confusion Matrix Practice 1-obhHCeHpysw.ar.vtt

387 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.es-ES.vtt

388 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.ja.vtt

389 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ja.vtt

389 B

Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.zh-CN.vtt

389 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.es-ES.vtt

389 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.zh-CN.vtt

389 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ja.vtt

390 B

Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt

390 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.es-ES.vtt

390 B

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt

390 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt

390 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.pt-BR.vtt

391 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt

392 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.pt-BR.vtt

393 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-hyAQ28MYmc4.th.vtt

393 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.es-ES.vtt

393 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.en.vtt

394 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.ar.vtt

395 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ja.vtt

395 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.pt-BR.vtt

396 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.ar.vtt

396 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ja.vtt

397 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.pt-BR.vtt

397 B

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.th.vtt

398 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.ar.vtt

399 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.zh-CN.vtt

399 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.en.vtt

399 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.ar.vtt

399 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.pt-BR.vtt

401 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-XN1eS7boCNg.ar.vtt

402 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.ja.vtt

403 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.zh-CN.vtt

403 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.zh-CN.vtt

403 B

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.ar.vtt

404 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.ar.vtt

405 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.pt-BR.vtt

405 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.es-ES.vtt

406 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.pt-BR.vtt

406 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.zh-CN.vtt

408 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.zh-CN.vtt

408 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-2cUiqlbt-hc.ar.vtt

408 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.it.vtt

409 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.pt-BR.vtt

410 B

Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.zh-CN.vtt

410 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt

410 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.pt-BR.vtt

410 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.zh-CN.vtt

411 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.ar.vtt

411 B

Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.pt-BR.vtt

411 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.es-ES.vtt

412 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.it.vtt

414 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.en.vtt

414 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.th.vtt

415 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ja.vtt

415 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.en.vtt

416 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.ja.vtt

417 B

Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.en.vtt

417 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.es-ES.vtt

417 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.pt-BR.vtt

418 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.en.vtt

419 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt

419 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ja.vtt

419 B

Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.zh-CN.vtt

419 B

Part 20-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt

420 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ja.vtt

420 B

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt

420 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.zh-CN.vtt

420 B

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.ar.vtt

420 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt

420 B

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.pt-BR.vtt

422 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.pt-BR.vtt

422 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.it.vtt

422 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.ar.vtt

422 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.zh-CN.vtt

422 B

Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.en.vtt

423 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-rRPwknIDuI0.th.vtt

423 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.en.vtt

424 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.es-ES.vtt

424 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.es-ES.vtt

425 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.zh-CN.vtt

425 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.hr.vtt

425 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.en.vtt

425 B

Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.zh-CN.vtt

426 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.es-ES.vtt

426 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.pt-BR.vtt

427 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.en.vtt

427 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.en.vtt

428 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ja.vtt

428 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.zh-CN.vtt

431 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.en.vtt

432 B

Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt

432 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.it.vtt

433 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.en.vtt

433 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.pt-BR.vtt

433 B

Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.en.vtt

434 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.en.vtt

435 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.it.vtt

435 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.ar.vtt

436 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.ar.vtt

437 B

Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt

437 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt

438 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.zh-CN.vtt

440 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.pt-BR.vtt

440 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.th.vtt

441 B

Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.zh-CN.vtt

441 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.pt-BR.vtt

442 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.zh-CN.vtt

442 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.pt-BR.vtt

444 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.it.vtt

444 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ja.vtt

444 B

Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.pt-BR.vtt

445 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.ar.vtt

445 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.zh-CN.vtt

446 B

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.th.vtt

447 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.pt-BR.vtt

447 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.en.vtt

449 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ja.vtt

449 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.es-ES.vtt

451 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.zh-CN.vtt

451 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.it.vtt

452 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.en.vtt

453 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt

453 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.hr.vtt

453 B

README.txt

454 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en.vtt

455 B

Part 12-Module 01-Lesson 16_Logistic Regression/24. False Positives in Eigenfaces-CMIM_Ocu8vg.ar.vtt

455 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.es-ES.vtt

455 B

Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.zh-CN.vtt

456 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.ar.vtt

456 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.it.vtt

456 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.zh-CN.vtt

456 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt

457 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ja.vtt

458 B

Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.zh-CN.vtt

458 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.es-ES.vtt

459 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.ar.vtt

459 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.zh-CN.vtt

460 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt

460 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.zh-CN.vtt

460 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.en.vtt

461 B

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.ar.vtt

462 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.ar.vtt

463 B

Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt

465 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.pt-BR.vtt

465 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.es-ES.vtt

466 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt

466 B

Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.en.vtt

466 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.zh-CN.vtt

467 B

Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt

467 B

Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.en.vtt

467 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.en.vtt

468 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.en.vtt

468 B

Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.en.vtt

469 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.hr.vtt

469 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.en.vtt

470 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.ar.vtt

471 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.es-ES.vtt

471 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.es-ES.vtt

471 B

Part 12-Module 01-Lesson 05_Binomial Distribution/02. Heads Tails-iyX0-eXStbw.ar.vtt

472 B

Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.en-US.vtt

472 B

Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.en.vtt

473 B

Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.zh-CN.vtt

473 B

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.pt-BR.vtt

473 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.ja.vtt

473 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.es-ES.vtt

473 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.ar.vtt

473 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.ar.vtt

473 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.pt-BR.vtt

473 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt

473 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.it.vtt

476 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.en.vtt

476 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.es-ES.vtt

478 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.pt-BR.vtt

478 B

Part 03-Module 01-Lesson 03_Training Neural Networks/10. Random Restart-idyBBCzXiqg.pt-BR.vtt

478 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt

478 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.en.vtt

479 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.th.vtt

480 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.es-ES.vtt

480 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.pt-BR.vtt

481 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt

481 B

Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt

481 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.ar.vtt

481 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.en-US.vtt

482 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.pt-BR.vtt

482 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ja.vtt

483 B

Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.en.vtt

484 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.en.vtt

486 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.pt-BR.vtt

488 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.zh-CN.vtt

490 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt

490 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ja.vtt

491 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.en.vtt

491 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.ar.vtt

491 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ja.vtt

492 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.zh-CN.vtt

492 B

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.en.vtt

493 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.ar.vtt

493 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.es-ES.vtt

493 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.pt-BR.vtt

495 B

Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt

495 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.zh-CN.vtt

495 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt

495 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.en.vtt

495 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.pt-BR.vtt

496 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.en.vtt

496 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.pt-BR.vtt

497 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.en.vtt

497 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.ar.vtt

497 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.pt-BR.vtt

498 B

Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.zh-CN.vtt

499 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.pt-BR.vtt

500 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.en.vtt

501 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt

501 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.pt-BR.vtt

501 B

Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt

501 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.es-ES.vtt

502 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.pt-BR.vtt

503 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.zh-CN.vtt

503 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.it.vtt

504 B

Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.zh-CN.vtt

504 B

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.zh-CN.vtt

505 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.en.vtt

508 B

Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.pt-BR.vtt

508 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.ar.vtt

509 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.en.vtt

513 B

Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.en.vtt

514 B

Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.pt-BR.vtt

514 B

Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt

514 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.ar.vtt

516 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.pt-BR.vtt

516 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.es-ES.vtt

516 B

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Projects-1-E_ZYovKeI.pt-BR.vtt

516 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.zh-CN.vtt

516 B

Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt

517 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.hr.vtt

517 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.ar.vtt

518 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.ar.vtt

518 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.zh-CN.vtt

521 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.zh-CN.vtt

522 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.zh-CN.vtt

522 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.pt-BR.vtt

522 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.pt-BR.vtt

523 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.en.vtt

523 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.pt-BR.vtt

523 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.ar.vtt

524 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt

524 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en.vtt

524 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.ja.vtt

525 B

Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.pt-BR.vtt

526 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.ja.vtt

527 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.en.vtt

527 B

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Projects-1-E_ZYovKeI.en.vtt

527 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.en.vtt

528 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.pt-BR.vtt

529 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.es-ES.vtt

529 B

Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.en.vtt

530 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.pt-BR.vtt

530 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.hr.vtt

530 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.zh-CN.vtt

531 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ja.vtt

531 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.es-ES.vtt

531 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.ar.vtt

532 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ja.vtt

532 B

Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.zh-CN.vtt

532 B

Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.zh-CN.vtt

532 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.pt-BR.vtt

533 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.es-ES.vtt

533 B

Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt

533 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.ar.vtt

533 B

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.en.vtt

534 B

Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.ar.vtt

534 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.es-ES.vtt

535 B

Part 12-Module 01-Lesson 16_Logistic Regression/12. Confusion Matrix Practice 2-HAQ0-Skvzmc.ar.vtt

535 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.zh-CN.vtt

536 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.en.vtt

536 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.zh-CN.vtt

536 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.en.vtt

537 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt

538 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.es-ES.vtt

538 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.zh-CN.vtt

538 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.hr.vtt

539 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt

540 B

Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.zh-CN.vtt

540 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.en.vtt

540 B

Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.pt-BR.vtt

541 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.th.vtt

541 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ja.vtt

541 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.ar.vtt

542 B

Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt

543 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.ar.vtt

544 B

Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt

545 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt

545 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.pt-BR.vtt

547 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.zh-CN.vtt

547 B

Part 12-Module 01-Lesson 04_Probability/10. Two Flips 3-3NSPqjp6pFY.ar.vtt

547 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.pt-BR.vtt

547 B

Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-jPspIs-fNxg.th.vtt

548 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt

548 B

Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt

548 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.zh-CN.vtt

549 B

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.ar.vtt

549 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.en.vtt

549 B

Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt

551 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt

551 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt

551 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.it.vtt

552 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.pt-BR.vtt

552 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.zh-CN.vtt

552 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.zh-CN.vtt

553 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.en.vtt

553 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.zh-CN.vtt

554 B

Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.th.vtt

554 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.en.vtt

555 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-CN.vtt

556 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt

556 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ja.vtt

557 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.ar.vtt

558 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.zh-CN.vtt

558 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.en.vtt

558 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.es-ES.vtt

560 B

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.zh-CN.vtt

561 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.ar.vtt

562 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.pt-BR.vtt

563 B

Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.en.vtt

564 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.hr.vtt

565 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.es-ES.vtt

565 B

Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.zh-CN.vtt

566 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.es-ES.vtt

566 B

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.pt-BR.vtt

567 B

Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.pt-BR.vtt

567 B

Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.zh-CN.vtt

569 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.en.vtt

569 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.pt-BR.vtt

570 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.it.vtt

571 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.en.vtt

571 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ja.vtt

572 B

Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.en.vtt

572 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.zh-CN.vtt

572 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt

574 B

Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.pt-BR.vtt

574 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.es-ES.vtt

575 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.hr.vtt

575 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.en.vtt

575 B

Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.pt-BR.vtt

576 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.zh-CN.vtt

577 B

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.th.vtt

578 B

Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.th.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.zh-Hans.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.zh-CN.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.pt-BR.vtt

582 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-FwaYsmnlLM4.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ja.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.ar.vtt

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Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.en.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt

586 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.ja.vtt

587 B

Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt

588 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ja.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.en.vtt

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Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.ar.vtt

591 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.pt-BR.vtt

593 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.ja.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.es-ES.vtt

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Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.ja.vtt

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Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.ja.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.zh-CN.vtt

597 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.pt-BR.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.en.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.it.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.en.vtt

599 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.ar.vtt

599 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.pt-BR.vtt

599 B

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt

599 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.hr.vtt

599 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt

600 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt

600 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.pt-BR.vtt

602 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-pT0FXiH_5nI.th.vtt

603 B

Part 01-Module 03-Lesson 01_Setting Up Your Computer/20. L2 02 Outro REPLACEMENT-W-6Se0G_FVE.en.vtt

603 B

Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.th.vtt

604 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt

607 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.ar.vtt

607 B

Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt

607 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.ar.vtt

608 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.ja.vtt

608 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.es-ES.vtt

608 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.zh-CN.vtt

609 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.en.vtt

610 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.es-ES.vtt

611 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.zh-CN.vtt

613 B

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt

615 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.ar.vtt

615 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.zh-CN.vtt

616 B

Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.zh-CN.vtt

616 B

Part 10-Module 01-Lesson 07_Working With Remotes/04. L1 - Git Push In Theory-21TvMEtMRys.zh-CN.vtt

616 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.zh-CN.vtt

616 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ru.vtt

617 B

Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.en.vtt

617 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ja.vtt

617 B

Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.zh-CN.vtt

617 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.en-US.vtt

617 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.en.vtt

617 B

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.pt-BR.vtt

617 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.it.vtt

618 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.pt-BR.vtt

618 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.ja.vtt

618 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt

618 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.pt-BR.vtt

620 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.pt-BR.vtt

620 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ja.vtt

621 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.en.vtt

622 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.zh-CN.vtt

622 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-zB2Y-5YEIec.ar.vtt

623 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.ja.vtt

623 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt

624 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt

624 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.it.vtt

625 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ja.vtt

625 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.ar.vtt

626 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ja.vtt

627 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.en.vtt

628 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.pt-BR.vtt

629 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.es-ES.vtt

629 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.zh-CN.vtt

629 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.en.vtt

630 B

Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt

631 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt

633 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.en.vtt

633 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.zh-CN.vtt

633 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.en.vtt

633 B

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.th.vtt

634 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt

634 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.en.vtt

635 B

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt

635 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.hr.vtt

637 B

Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt

638 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.zh-CN.vtt

638 B

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.zh-CN.vtt

639 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.en.vtt

639 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.hr.vtt

640 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-64EjAbqrtmo.ar.vtt

640 B

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.en.vtt

641 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.pt-BR.vtt

642 B

Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.zh-CN.vtt

643 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.zh-CN.vtt

643 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.zh-CN.vtt

644 B

Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.en.vtt

645 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.en.vtt

646 B

Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.pt-BR.vtt

646 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.ar.vtt

650 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.ar.vtt

650 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.zh-CN.vtt

650 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.en.vtt

650 B

Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.en.vtt

651 B

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.en.vtt

651 B

Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.ar.vtt

652 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.th.vtt

653 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.pt-BR.vtt

653 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.en.vtt

653 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.ar.vtt

654 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.th.vtt

654 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt

655 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.en.vtt

655 B

Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.en.vtt

655 B

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.ar.vtt

656 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.it.vtt

656 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.pt.vtt

656 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.es-ES.vtt

656 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.ja.vtt

657 B

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.th.vtt

657 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.en.vtt

658 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.zh-CN.vtt

658 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.ja.vtt

661 B

Part 12-Module 01-Lesson 16_Logistic Regression/25. False Negatives in Eigenfaces-bxDutNyYKjE.ar.vtt

662 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.ar.vtt

662 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.hr.vtt

662 B

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.ar.vtt

663 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.en.vtt

663 B

Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.zh-CN.vtt

664 B

Part 10-Module 01-Lesson 07_Working With Remotes/04. L1 - Git Push In Theory-21TvMEtMRys.en.vtt

665 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.es-ES.vtt

665 B

Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-hqO8kxRJdd4.ar.vtt

665 B

Part 10-Module 01-Lesson 07_Working With Remotes/04. L1 - Git Push In Theory-21TvMEtMRys.pt-BR.vtt

666 B

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.th.vtt

667 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.ja.vtt

669 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.zh-CN.vtt

670 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.ar.vtt

670 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.es-ES.vtt

671 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.it.vtt

671 B

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.en.vtt

671 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.es-ES.vtt

671 B

Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.ar.vtt

673 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.pt-BR.vtt

673 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.zh-CN.vtt

673 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.zh-CN.vtt

675 B

Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.en.vtt

675 B

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.ar.vtt

675 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.zh-CN.vtt

676 B

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.th.vtt

677 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt

678 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.ar.vtt

679 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.es-ES.vtt

679 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.es-ES.vtt

679 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.pt-BR.vtt

680 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.es-ES.vtt

681 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.es-ES.vtt

682 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.en.vtt

682 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.en.vtt

682 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.en.vtt

682 B

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.ar.vtt

683 B

Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.ar.vtt

684 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.ar.vtt

684 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.ar.vtt

684 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-BR.vtt

684 B

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.en.vtt

685 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.es-ES.vtt

686 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.pt-BR.vtt

686 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.pt-BR.vtt

687 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.en.vtt

688 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.pt-BR.vtt

688 B

Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt

690 B

Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.en.vtt

690 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.hr.vtt

690 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.pt-BR.vtt

691 B

Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.pt-BR.vtt

692 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.pt-BR.vtt

692 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.pt-BR.vtt

694 B

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt

694 B

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.pt-BR.vtt

694 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.pt-BR.vtt

695 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.it.vtt

695 B

Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.zh-CN.vtt

695 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.pt-BR.vtt

695 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.pt-BR.vtt

696 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.it.vtt

697 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.zh-CN.vtt

698 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.pt-BR.vtt

699 B

Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.en.vtt

700 B

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.th.vtt

700 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.zh-CN.vtt

701 B

Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt

701 B

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.pt-BR.vtt

702 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.it.vtt

702 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.en.vtt

704 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.zh-CN.vtt

704 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt

705 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.pt-BR.vtt

705 B

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.ar.vtt

706 B

Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.zh-CN.vtt

707 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.ja.vtt

708 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.zh-CN.vtt

708 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.ja.vtt

710 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.it.vtt

712 B

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.zh-CN.vtt

713 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.en.vtt

714 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.en.vtt

715 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.en.vtt

715 B

Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.zh-CN.vtt

715 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pl.vtt

715 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.ja.vtt

716 B

Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt

716 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/07. Accuracy 2-ueYCLfd_aNQ.en-US.vtt

716 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.ar.vtt

717 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.ja.vtt

717 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.th.vtt

717 B

Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt

719 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt

719 B

Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.en.vtt

720 B

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.th.vtt

723 B

Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt

723 B

Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.zh-CN.vtt

724 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt

725 B

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/04. Intro To MovieTweetings-cuXvLIkq_W8.en.vtt

725 B

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.ar.vtt

726 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.ar.vtt

727 B

Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt

727 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.es-ES.vtt

728 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.pt-BR.vtt

728 B

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.th.vtt

729 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.es-ES.vtt

729 B

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/17. Funk SVD Review-nc3GMIrISHE.en.vtt

729 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.ja.vtt

730 B

Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.ar.vtt

731 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.ar.vtt

731 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.en.vtt

734 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.pt-BR.vtt

735 B

Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.pt-BR.vtt

735 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.zh-CN.vtt

736 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.en.vtt

736 B

Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.zh-CN.vtt

736 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.pt-BR.vtt

736 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.pt-BR.vtt

737 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.pt-BR.vtt

737 B

Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.en.vtt

737 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.en.vtt

738 B

Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.ar.vtt

738 B

Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt

739 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt

739 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.zh-CN.vtt

741 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.es-ES.vtt

741 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.pt-BR.vtt

741 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.it.vtt

742 B

Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.zh-CN.vtt

742 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.zh-CN.vtt

745 B

Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.pt-BR.vtt

745 B

Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.zh-CN.vtt

745 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.es-ES.vtt

746 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.es-ES.vtt

746 B

Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.pt-BR.vtt

746 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.pt-BR.vtt

747 B

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.ar.vtt

747 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.hr.vtt

748 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.ja.vtt

748 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.es-ES.vtt

748 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.zh-CN.vtt

750 B

Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.zh-CN.vtt

750 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.zh-CN.vtt

751 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.en.vtt

752 B

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.zh-CN.vtt

752 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.ar.vtt

752 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.pt-BR.vtt

753 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.pt-BR.vtt

753 B

Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.en.vtt

754 B

Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.en.vtt

756 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.es-ES.vtt

757 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.zh-CN.vtt

759 B

Part 08-Module 01-Lesson 07_Visualization Case Study/01. L7 011 Intro V1-Virihwp36do.pt-BR.vtt

759 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.ja.vtt

760 B

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.zh-CN.vtt

761 B

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.pt-BR.vtt

761 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.ja.vtt

762 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.zh-CN.vtt

763 B

Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.en.vtt

763 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.it.vtt

764 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.pt-PT.vtt

765 B

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.ar.vtt

765 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.it.vtt

767 B

Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.pt-BR.vtt

767 B

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.th.vtt

768 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.ar.vtt

768 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.pt-BR.vtt

769 B

Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.pt-BR.vtt

769 B

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt

769 B

Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt

771 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.th.vtt

772 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.en.vtt

772 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.pt-BR.vtt

774 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.zh-CN.vtt

775 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.es-ES.vtt

775 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.th.vtt

777 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.es-ES.vtt

777 B

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.th.vtt

779 B

Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.pt-BR.vtt

780 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.zh-CN.vtt

780 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.en.vtt

781 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.en.vtt

782 B

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.ar.vtt

783 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.zh-CN.vtt

785 B

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt

786 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.zh-CN.vtt

787 B

Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.ar.vtt

787 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.zh-CN.vtt

787 B

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.en.vtt

789 B

Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt

790 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.en.vtt

790 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt

790 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.en.vtt

790 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.pt-BR.vtt

792 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.hr.vtt

792 B

Part 02-Module 01-Lesson 02_Linear Regression/17. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt

793 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.pt-BR.vtt

797 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.it.vtt

797 B

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.en.vtt

798 B

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.en.vtt

799 B

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.ar.vtt

799 B

Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.pt-BR.vtt

800 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.ar.vtt

802 B

Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.pt-BR.vtt

802 B

Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt

804 B

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.en-GB.vtt

804 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt

804 B

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.ja.vtt

805 B

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/01. 01 Intro V1 V3-Zl_es7xtSqk.en.vtt

806 B

Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.en.vtt

806 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.ar.vtt

807 B

Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.pt-BR.vtt

810 B

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt

812 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.zh-CN.vtt

812 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.ja.vtt

813 B

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.th.vtt

813 B

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt

813 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt

813 B

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. L2 21 Conclusion V1 V1-anPnokWZOZQ.en.vtt

816 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.ar.vtt

816 B

Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.en.vtt

817 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.es-ES.vtt

817 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.zh-CN.vtt

817 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.en.vtt

818 B

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.ar.vtt

819 B

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.ja.vtt

819 B

Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.pt-BR.vtt

820 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.en.vtt

821 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/08. CONCAT-bCxZnQN28Y4.ar.vtt

822 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.es-ES.vtt

823 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt

824 B

Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.en.vtt

824 B

Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.en.vtt

824 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.pt-BR.vtt

825 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.en.vtt

826 B

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color5.png

826 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.en.vtt

826 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.zh-CN.vtt

828 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.ja.vtt

828 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.en.vtt

828 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.ar.vtt

829 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.pt-BR.vtt

829 B

Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.pt-BR.vtt

830 B

Part 02-Module 01-Lesson 02_Linear Regression/17. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt

831 B

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.it.vtt

832 B

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.pt-BR.vtt

834 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.ja.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.en.vtt

836 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.es-ES.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.it.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 54 Content On Different Branches-Px6EUylw8Uw.ar.vtt

837 B

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.ar.vtt

838 B

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color3.png

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Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.en.vtt

839 B

Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.zh-CN.vtt

840 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt

840 B

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.pt-BR.vtt

841 B

Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.ja.vtt

841 B

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt

842 B

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.pt-BR.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color4.png

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.ar.vtt

844 B

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.es-ES.vtt

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Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.ar.vtt

845 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.ja.vtt

846 B

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.ar.vtt

847 B

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/02. Identifying Recommendation Engines-KwegrgvV-V4.en.vtt

847 B

Part 07-Module 01-Lesson 03_SQL Aggregations/09. MIN MAX-1ewVsgWUih8.pt-BR.vtt

847 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.pt-BR.vtt

848 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.zh-CN.vtt

848 B

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.ar.vtt

850 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.es-ES.vtt

850 B

Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.ar.vtt

851 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.zh-CN.vtt

852 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.hr.vtt

854 B

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.en.vtt

855 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.en.vtt

856 B

Part 08-Module 01-Lesson 07_Visualization Case Study/07. L7 0F1 Congrats V3-LF-obnL7CI0.pt-BR.vtt

859 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.ar.vtt

861 B

Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.zh-CN.vtt

862 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.pt-BR.vtt

864 B

Part 17-Module 02-Lesson 03_AB Testing Case Study/14. Conclusion-2G6x3oQnjy4.en.vtt

865 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.pt-BR.vtt

866 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.it.vtt

866 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.pt-BR.vtt

867 B

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.en.vtt

867 B

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.en-GB.vtt

868 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.zh-CN.vtt

869 B

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.es-ES.vtt

870 B

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.pt-BR.vtt

871 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.ja.vtt

872 B

Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.pt-BR.vtt

874 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.en.vtt

874 B

Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.pt-BR.vtt

874 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.pt-BR.vtt

879 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.en.vtt

879 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.en.vtt

879 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.es-ES.vtt

880 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.zh-CN.vtt

883 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.en.vtt

883 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.pt-BR.vtt

884 B

Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.en.vtt

884 B

Part 16-Module 01-Lesson 02_ETL Pipelines/05. 05 Extraction Idea 1 V1 V2-4dKG_08zMm4.en.vtt

885 B

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.es-ES.vtt

885 B

Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.ar.vtt

885 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.zh-CN.vtt

886 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.ar.vtt

886 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.es-ES.vtt

886 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.ja.vtt

887 B

Part 12-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.zh-CN.vtt

887 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-8QEAYYIyopY.zh-CN.vtt

888 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.pt-BR.vtt

889 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/05. Confusion-Matrix-Solution-ywwSzyU9rYs.pt-BR.vtt

889 B

Part 12-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.zh-CN.vtt

890 B

Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.zh-CN.vtt

892 B

Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.zh-CN.vtt

892 B

Part 10-Module 01-Lesson 07_Working With Remotes/06. Pull Vs Fetch-kxXdk2HcOBo.zh-CN.vtt

893 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.zh-CN.vtt

893 B

Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.ar.vtt

894 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.ar.vtt

896 B

Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.pt-BR.vtt

896 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.zh-CN.vtt

896 B

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.en.vtt

898 B

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.en.vtt

898 B

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.pt-BR.vtt

899 B

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.ar.vtt

899 B

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.ar.vtt

900 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.es-ES.vtt

900 B

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.en.vtt

900 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.ar.vtt

902 B

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.ja.vtt

902 B

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.pt-BR.vtt

902 B

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.ar.vtt

903 B

Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.ar.vtt

904 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.zh-CN.vtt

905 B

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/23. 24 Conclusion V1 V2-Jq6pj_uKDmY.en.vtt

905 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.en.vtt

906 B

Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.en.vtt

906 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.zh-CN.vtt

908 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.en.vtt

908 B

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.th.vtt

910 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.ja.vtt

911 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.zh-CN.vtt

912 B

Part 10-Module 01-Lesson 07_Working With Remotes/06. Pull Vs Fetch-kxXdk2HcOBo.en.vtt

913 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.zh-CN.vtt

913 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.zh-CN.vtt

913 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.zh-CN.vtt

914 B

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.pt-BR.vtt

914 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.it.vtt

915 B

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.ar.vtt

916 B

Part 02-Module 01-Lesson 03_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.zh-CN.vtt

916 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.pt-BR.vtt

917 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.en.vtt

918 B

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.en.vtt

918 B

Part 20-Module 01-Lesson 01_Neural Networks/img/codecogseqn-58.gif

919 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.pt-BR.vtt

919 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/codecogseqn-58.gif

919 B

Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.pt-BR.vtt

920 B

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/05. Machine Learning Workflow-0nA6oTIlwaA.en.vtt

921 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.zh-CN.vtt

922 B

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.ja.vtt

923 B

Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.pt-BR.vtt

925 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.hr.vtt

925 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.zh-CN.vtt

927 B

Part 10-Module 01-Lesson 07_Working With Remotes/04. L1 - Git Push In Theory-21TvMEtMRys.ar.vtt

927 B

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/19. Conclusion-_ATzG6khLdk.en.vtt

927 B

Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.zh-CN.vtt

928 B

Part 02-Module 01-Lesson 03_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.pt-BR.vtt

928 B

Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.zh-CN.vtt

930 B

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-8QEAYYIyopY.pt-BR.vtt

930 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.en.vtt

931 B

Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.zh-CN.vtt

933 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.pt-BR.vtt

933 B

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.pt-BR.vtt

935 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.zh-CN.vtt

935 B

Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.pt-BR.vtt

935 B

Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.en.vtt

937 B

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.pt-BR.vtt

937 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.pt-BR.vtt

937 B

Part 10-Module 01-Lesson 07_Working With Remotes/06. Pull Vs Fetch-kxXdk2HcOBo.pt-BR.vtt

938 B

Part 04-Module 01-Lesson 05_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.en.vtt

938 B

Part 02-Module 01-Lesson 02_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.en.vtt

939 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.en.vtt

940 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.en.vtt

940 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.en.vtt

940 B

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.ar.vtt

941 B

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.hr.vtt

942 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.ar.vtt

942 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.pt-BR.vtt

944 B

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.zh-CN.vtt

944 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.pt-BR.vtt

944 B

Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.en.vtt

944 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.pt-BR.vtt

945 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.it.vtt

945 B

Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.zh-CN.vtt

946 B

Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.ar.vtt

946 B

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt

947 B

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt

947 B

Part 10-Module 01-Lesson 03_Review a Repo's History/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.zh-CN.vtt

948 B

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.es-ES.vtt

948 B

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.es-ES.vtt

948 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.ja.vtt

949 B

Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.zh-CN.vtt

949 B

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.en.vtt

952 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.ja.vtt

952 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.pt-BR.vtt

953 B

Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.ar.vtt

953 B

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.en.vtt

953 B

Part 04-Module 01-Lesson 05_Random Projection and ICA/07. L6 5 ICA Implementation V1 V1-fZGxYfJmKaE.pt-BR.vtt

955 B

Part 02-Module 01-Lesson 02_Linear Regression/17. DLND REG 12 Absolute Vs Squared Error 2 V1 (1)-7El1OH17Oi4.pt-BR.vtt

956 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.hr.vtt

956 B

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.ja.vtt

957 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.hr.vtt

957 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.es-ES.vtt

957 B

Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.en.vtt

958 B

Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.en.vtt

959 B

Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.ar.vtt

959 B

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/05. Confusion-Matrix-Solution-ywwSzyU9rYs.zh-CN.vtt

959 B

Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.pt-BR.vtt

962 B

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.zh-CN.vtt

962 B

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.ar.vtt

963 B

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.en.vtt

963 B

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.zh-CN.vtt

964 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.zh-CN.vtt

964 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.en.vtt

964 B

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.pt-BR.vtt

965 B

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.zh-CN.vtt

966 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.es-ES.vtt

967 B

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.it.vtt

967 B

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.it.vtt

969 B

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.th.vtt

970 B

Part 02-Module 01-Lesson 02_Linear Regression/17. DLND REG 13 Absolute Vs Squared Error 3 V1 (1)-bIVGf_dDkrY.pt-BR.vtt

970 B

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Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.ja.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/05. Confusion-Matrix-Solution-ywwSzyU9rYs.en-US.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/13. Named Entity Recognition-QUQu2nsE7vE.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/27. WHERE with Non-Numeric Data-_pLx7MHOyjo.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.es-ES.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/24. Working With Missing Values-mbAgYicmzqE.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.hr.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/14. Learning Rate-TwJ8aSZoh2U.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/20. Precision and Recall-3vT0kSBCLdU.en.vtt

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Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.ar.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.it.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/31. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.en.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.hr.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/06. Onward-iXbMaTwfIJI.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.zh-CN.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.zh-CN.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/20. Precision and Recall-3vT0kSBCLdU.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.ja.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.zh-CN.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.ar.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/09. Local Minima-gF_sW_nY-xw.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.en.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.en.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/07. Scikit Learn-kxvmG8ZsOVg.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.ja.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/03. Entity Relationship Diagrams-YY2TAJLEINA.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.th.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/06. DL 53 Q Regularization-KxROxcRsHL8.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.en.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/06. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 17 Git The Big Picture 2-rFtUkk-sCqw.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.en.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/07. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.th.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.es-MX.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Introduction-TRw4bvZuEG8.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/05. Moving A Line-8EIHFyL2Log.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.ja.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.zh-CN.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.es-ES.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/16. Error Functions Around the World-34AAcTECu2A.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.en.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.it.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/01. Natural Language Processing-UQBxJzoCp-I.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/01. Introduction-k7YOVTkFRJM.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/06. SUM-0zUP14PeiXk.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.th.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 17 Git The Big Picture 2-rFtUkk-sCqw.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.es-ES.vtt

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Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/01. Introduction-k7YOVTkFRJM.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.ar.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/06. Up and Running On Medium-0QzbxjAcMq0.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.zh-CN.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/08. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.ar.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.es-ES.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.zh-CN.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/e.gif

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Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/24. Polynomial Regression-DBhWG-PagEQ.pt-BR.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.zh-CN.vtt

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Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.ar.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Introduction-TRw4bvZuEG8.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.th.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/10. ALIAS-viWHJaxWTvw.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.en.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/07. Outro-ot4fPX1jzOI.en.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. L5 061 Other Adaptations Of Bivariate Plots V3-qanSZttNzFM.en.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/11. Transform Walk Through-i9_0kHCCCCE.en.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.zh-CN.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/09. What's Ahead-2Hxy2Jlu8nk.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.pt-BR.vtt

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.pt-BR.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt

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Part 04-Module 01-Lesson 05_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/22. One-Hot Encoding-a0j1CDXFYZI.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/10. AVG-diqCDztOL64.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.it.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.ar.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.en.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.ja.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/24. Working With Missing Values-mbAgYicmzqE.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.ar.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.ar.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 1-SNFHYbJvlZU.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.pt-BR.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/06. Pull Vs Fetch-kxXdk2HcOBo.ar.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.zh-CN.vtt

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Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.zh-CN.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/06. Up and Running On Medium-0QzbxjAcMq0.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/17. Summary-zKYEvRd2XmI.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/13. Named Entity Recognition-QUQu2nsE7vE.pt-BR.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/43. Outro V1 V4-XE3aoYOXeBw.en.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/09. What's Ahead-2Hxy2Jlu8nk.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/20. Predicting Salary-g1ZAn02ETK4.en.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/08. L2 2 11 Logging V2-9qKQdRoIMbU.pt-BR.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.zh-CN.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.en.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/15. Captivate Your Audience - End With A Call To Action-EajX2NbHJ6w.pt-BR.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/23. Word Embeddings-4mM_S9L2_JQ.zh-CN.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/02. First Things First-ehjC7JK-zMI.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/06. Outro-xj70jX9Moxs.pt-BR.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/07. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/14. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/05. Outro-xj70jX9Moxs.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.en-GB.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.ar.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.ar.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.en.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Sending Branches To Remote-414f0ukhOTY.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/08. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.en.vtt

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Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.zh-CN.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.ja.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. L5 061 Other Adaptations Of Bivariate Plots V3-qanSZttNzFM.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/27. WHERE with Non-Numeric Data-_pLx7MHOyjo.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/16. Feature Scaling-rpTVp7C8AXo.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.ar.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.it.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.ar.vtt

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.ar.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.pt-BR.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/05. Outro-xj70jX9Moxs.en.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.zh-CN.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/06. Outro-xj70jX9Moxs.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.es-ES.vtt

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Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.zh-CN.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/24. Polynomial Regression-DBhWG-PagEQ.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/19. DISTINCT-YDJEHkgKORY.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/24. Modeling-P4w_2rkxBvE.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.pt-BR.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/01. Natural Language Processing-UQBxJzoCp-I.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/31. Non-Linear Models-HWuBKCZsCo8.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.en.vtt

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Part 04-Module 01-Lesson 05_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/03. What is the Difference-I3tQvrCgNrQ.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.zh-CN.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/codecogseqn-62.gif

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.ar.vtt

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Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.ar.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.es-ES.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/02. CRISP-DM-PaVwnGcqlSE.en.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.ja.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.ar.vtt

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Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.ar.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.zh-CN.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/02. First Things First-ehjC7JK-zMI.pt-BR.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.en.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/29. Continuous Perceptrons-07-JJ-aGEfM.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.pt-BR.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.en.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.ar.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.zh-CN.vtt

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Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.ar.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.zh-CN.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.es-ES.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.es-ES.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.pt-BR.vtt

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Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.pt-BR.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.zh-CN.vtt

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Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.en.vtt

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Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. DataVis L3 11 V1-C8DGwJa_adA.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.hr.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.zh-CN.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.zh-CN.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.ar.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/20. Precision and Recall-3vT0kSBCLdU.ar.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.th.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.en.vtt

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Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.ar.vtt

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Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.ja.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.pt-BR.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/02. Lesson Overview -q1beUVlLoIQ.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.it.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. L5 091 Feature Engineering V2-jpMOSFMMga4.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. DataVis L3 11 V1-C8DGwJa_adA.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.zh-CN.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/02. L2 2 02 Testing V1 V1-IkLUUHt_jis.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/08. Why Neural Networks-zAkzOZntK6Y.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/10. AVG-diqCDztOL64.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/18. L2 181 Lesson Summary HDmp4 V3-kKEeBDs4HuM.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/31. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.zh-CN.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/01. L2 01 Intro V1 V1-z7v7oa--W48.pt-BR.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.zh-CN.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.ar.vtt

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Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.pt-BR.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.ja.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.zh-CN.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.ar.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/22. One-Hot Encoding-a0j1CDXFYZI.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/21. 29 Missing Data Delete V1 V2-L0MoPGyiiYo.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt

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Part 15-Module 01-Lesson 06_Web Development/19. The World Wide Web-Rxn-zCyg_iA.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.pt-BR.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt

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Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.es-ES.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/y.gif

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/04. Fitting A Line-gkdoknEEcaI.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/06. SUM-0zUP14PeiXk.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/27. WHERE with Non-Numeric Data-_pLx7MHOyjo.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/01. Intro-j5RmK0UHOTY.ar.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/10. L6 131 Lesson Summary V1-t6ss31RZF34.pt-BR.vtt

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Part 15-Module 01-Lesson 06_Web Development/19. The World Wide Web-Rxn-zCyg_iA.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/45. The Data Science Process Evaluate And Deploy-sxT43JlH_eM.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.it.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/02. CRISP-DM-PaVwnGcqlSE.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/07. Outro-ot4fPX1jzOI.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/04. COUNT-b4FCWAEGmLg.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/10. AVG-diqCDztOL64.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.zh-CN.vtt

1.4 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/06. L1 - Fetch Merge And Push-jwyQUfE1Eqw.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/06. Onward-iXbMaTwfIJI.ar.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.ar.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/24. Modeling-P4w_2rkxBvE.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.es-ES.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.zh-CN.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.es-ES.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.ar.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.ar.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.zh-CN.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/20. Predicting Salary-g1ZAn02ETK4.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/02. 02 Intro SC V1-mIgABrjJVBY.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.pt-BR.vtt

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Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/21. 29 Missing Data Delete V1 V2-L0MoPGyiiYo.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt

1.5 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.ar.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt

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Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt

1.5 kB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.en.vtt

1.5 kB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Conclusions-3IFF1GzUq0Y.en.vtt

1.5 kB

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.pt-BR.vtt

1.5 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/03. What is the Difference-I3tQvrCgNrQ.pt-BR.vtt

1.5 kB

Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.ja.vtt

1.5 kB

Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.ar.vtt

1.5 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. L5 091 Feature Engineering V2-jpMOSFMMga4.pt-BR.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.zh-CN.vtt

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Part 04-Module 01-Lesson 01_Clustering/16. Feature Scaling-rpTVp7C8AXo.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.en.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.en.vtt

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Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.en.vtt

1.5 kB

Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.zh-CN.vtt

1.5 kB

Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.en.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.en.vtt

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Part 04-Module 01-Lesson 04_PCA/17. When to Use PCA-arSP83-CM6w.pt-BR.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.zh-CN.vtt

1.5 kB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.pt-BR.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/03. Entity Relationship Diagrams-YY2TAJLEINA.ar.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.en.vtt

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Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.hr.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt

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Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.pt-BR.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.zh-CN.vtt

1.5 kB

Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt

1.5 kB

Part 10-Module 01-Lesson 07_Working With Remotes/06. L1 - Fetch Merge And Push-jwyQUfE1Eqw.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.pt-BR.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/06. L1 - Fetch Merge And Push-jwyQUfE1Eqw.en.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.ar.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Git Pull In Theory-MjNU2LTDVAA.ar.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.en.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.pt-BR.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.ar.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. L3 081 Histograms V2-RLez9L0htGQ.zh-CN.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.ja.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.es-ES.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.ar.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.pt-BR.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.zh-CN.vtt

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Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 03 Tagging Overview-D4VdXT72ASE.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.pt-BR.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/09. L6 10 V1 V6-LoYT4NMSPGk.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.en.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/01. L6 011 Intro V1-gLy8qpursJI.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.th.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.en.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.es-ES.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/23. Word Embeddings-4mM_S9L2_JQ.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.ar.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.zh-CN.vtt

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/10. Parting Words Of Encouragement-sFF_WOnpsXM.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/13. Parting Words Of Encouragement-sFF_WOnpsXM.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.pt-BR.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. DataVis L5C05 V1-v19gCP4TvwE.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.pt-BR.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 17 Git The Big Picture 2-rFtUkk-sCqw.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.pt-BR.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.en.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/07. Ud206 008 Shell P5 - Parameters-UX9mzq11Mmg.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/45. The Data Science Process Evaluate And Deploy-sxT43JlH_eM.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.en.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/19. 15 Pipelines And Grid Search V1 V3-HZaOiSxJjCY.en.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt 2-0qcJ_oggdKw.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.en.vtt

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/09. Capstone-bq-H7M5BU3U.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/07. Ud206 008 Shell P5 - Parameters-UX9mzq11Mmg.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.en.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 03 Tagging Overview-D4VdXT72ASE.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. Data Vis L4 C07 V1-f6v3L3IDo24.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/22. One-Hot Encoding-a0j1CDXFYZI.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/05. COUNT NULLs-ngxgqfFFFLQ.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/43. Outro V1 V4-XE3aoYOXeBw.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.ja.vtt

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Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.pt-BR.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/f6.gif

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Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.en.vtt

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Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.th.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.en.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/02. Lesson Overview -q1beUVlLoIQ.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.ar.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.pt-BR.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.zh-CN.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/11. SMART Mnemonic-B0Bnxyu2aKM.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. DataVis L5C05 V1-v19gCP4TvwE.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/04. Business And Data Understanding - Example-bXQTGS61BU8.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/17. Feature Scaling Example--Axyt0bPCT0.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.pt-BR.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 1 Advantages Of Using Pipeline V1 V2-ASYcx911E2Q.en.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.en.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.ja.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.pt-BR.vtt

1.6 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/04. COUNT-b4FCWAEGmLg.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.ja.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/05. KMeans-B9jdQFpPEk0.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.ar.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.zh-CN.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.zh-CN.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.en.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.en.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. L6 061 Polishing Plots V3-4TixzVx79uk.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.zh-CN.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.en.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.it.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/20. Outro SC V1-YD1grQje9fw.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.th.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.zh-CN.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.pt-BR.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.en.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.en.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 03 Tagging Overview-D4VdXT72ASE.en.vtt

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Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/04. Business And Data Understanding - Example-bXQTGS61BU8.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.ar.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.en.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/34. Scaling Data-OgjTk3XCUUE.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.ar.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/backprop-weight-update.gif

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Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.hr.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/07. Ud206 008 Shell P5 - Parameters-UX9mzq11Mmg.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.ja.vtt

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/10. Parting Words Of Encouragement-sFF_WOnpsXM.pt-BR.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/13. Parting Words Of Encouragement-sFF_WOnpsXM.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.es-ES.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.es-ES.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.zh-CN.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/02. L2 2 02 Testing V1 V1-IkLUUHt_jis.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.th.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.ar.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.ar.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.zh-CN.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/04. Practical Significance-eJ3idt3AJ7E.en.vtt

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Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.pt-BR.vtt

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/02. Disaster Relief Project Preview-DuwYAjqGM3E.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.pt-BR.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/01. Introduction-Yg0gBpTzkMo.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.ja.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.en.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.zh-CN.vtt

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Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.ar.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.pt-BR.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/22. L2 18 Version Control Git Commit Messages V1 V2-w1iHWpwOkMg.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/23. Word Embeddings-4mM_S9L2_JQ.pt-BR.vtt

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Part 19-Module 01-Lesson 01_Congratulations!/01. Congrats-OTp4YOTDd0Q.en.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.en.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/20. Outro SC V1-YD1grQje9fw.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.pt-BR.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/17. 05 Docstrings V1-_gapemxsRJY.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.zh-CN.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.pt-BR.vtt

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.zh-CN.vtt

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/12. Types Of Collaborative Filtering-fZhkWHHP6SM.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.en.vtt

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Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.zh-CN.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.en.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.en.vtt

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Part 04-Module 01-Lesson 04_PCA/01. Introduction-tpFPcxoGxaE.pt-BR.vtt

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.ar.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.es-ES.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.zh-CN.vtt

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Part 04-Module 01-Lesson 01_Clustering/03. Two Types of Unsupervised Learning-aHK_rpaS_ts.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt

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Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.zh-CN.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/12. 12 Pipelines And Feature Unions V1 V3-zduxy0g23L0.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.ar.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/hidden-layer-weights.gif

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.en.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c03-practicalsignificance-01.png

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.zh-CN.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.zh-CN.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.zh-CN.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/19. DISTINCT-YDJEHkgKORY.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.it.vtt

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Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.en.vtt

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.en.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.zh-CN.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.ja.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.pt-BR.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.pt-BR.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.zh-CN.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/03. Two Types of Unsupervised Learning-aHK_rpaS_ts.pt-BR.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.pt-BR.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.pt-BR.vtt

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Part 04-Module 01-Lesson 01_Clustering/17. Feature Scaling Example--Axyt0bPCT0.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.th.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.ja.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.zh-CN.vtt

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Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/38. Bloopers Intro 1 V1-Y1weHponR2Q.en.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.es-ES.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.pt-BR.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.zh-CN.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/01. L5 011 Intro V3-ckylQMBXB10.en.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/07. How Databases Store Data-H0C9z_sRvLE.ar.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.zh-CN.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/12. Conclusions-yMRRXDKb428.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.ar.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/01. Introduction-Yg0gBpTzkMo.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.zh-CN.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.en.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/04. COUNT-b4FCWAEGmLg.ar.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/10. AVG-diqCDztOL64.ar.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/23. Putting It All Together-r5jfD2uKnbQ.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.zh-CN.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.zh-CN.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.zh-CN.vtt

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Part 15-Module 01-Lesson 06_Web Development/26. Flask Pandas Plotly Part3-e8owK5zk-g8.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. Data Vis L4 C07 V1-f6v3L3IDo24.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/03. What is the Difference-I3tQvrCgNrQ.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/39. Load Walk Through-AZvC7kYp_74.en.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/14. 22 Cleaning Data V1 V3-zYxgkUqTX0Y.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.pt-BR.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/01. Intro-EBGMcpWe8-U.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/07. 07 Changing K 1 V3-Bd3M-xUlqEI.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/28. T-SNE-xxcK8oZ6_WE.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.th.vtt

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Part 04-Module 01-Lesson 04_PCA/06. How to Reduce Features-ydhrelgjriI.pt-BR.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.ar.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/15. L4 151 Lesson Summary V1-5igqM44KEmw.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.es-ES.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.ja.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.ja.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.zh-CN.vtt

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Part 04-Module 01-Lesson 04_PCA/06. How to Reduce Features-ydhrelgjriI.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/05. COUNT NULLs-ngxgqfFFFLQ.en.vtt

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Part 04-Module 01-Lesson 01_Clustering/05. KMeans-B9jdQFpPEk0.pt-BR.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.pt-BR.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Operator-3vLGEuXAAvA.zh-CN.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.it.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/39. Load Walk Through-AZvC7kYp_74.pt-BR.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/10. ALIAS-viWHJaxWTvw.ar.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/03. Your First JOIN-HkX9fkNRbU8.zh-CN.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.ar.vtt

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Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.pt-BR.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.pt-BR.vtt

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Part 15-Module 01-Lesson 06_Web Development/04. The Front End-CspuxLGFM4U.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.ar.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/f2.gif

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.en.vtt

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Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.ar.vtt

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Part 04-Module 01-Lesson 04_PCA/01. Introduction-tpFPcxoGxaE.en.vtt

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.en.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.en.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.pt-BR.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.zh-CN.vtt

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Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.zh-CN.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.zh-CN.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/01. L5 011 Intro V3-ckylQMBXB10.pt-BR.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/20. 28 Missing Data Causes V1 V2-zlw8ESS6Q88.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.ar.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.pt-BR.vtt

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. 13 Inheritance Example V1-uWT-HIHBjv0.pt-BR.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.en.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/26. Conclusion-R5-OYqKk9Ys.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/01. 01 Intro V1 2 V4-iW4uqhfRk10.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.en.vtt

1.9 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.pt-BR.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.en.vtt

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Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.zh-CN.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.zh-CN.vtt

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Part 15-Module 01-Lesson 06_Web Development/26. Flask Pandas Plotly Part3-e8owK5zk-g8.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. 13 Inheritance Example V1-uWT-HIHBjv0.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.en.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 1 Advantages Of Using Pipeline V1 V2-ASYcx911E2Q.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.en-GB.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt

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Part 04-Module 01-Lesson 01_Clustering/07. 07 Changing K 1 V3-Bd3M-xUlqEI.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.th.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.pt-BR.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/07. Ud206 008 Shell P5 - Parameters-UX9mzq11Mmg.ar.vtt

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Part 15-Module 01-Lesson 06_Web Development/01. L4 Intro V2--PGMIIXFCgg.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.es-ES.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/34. Scaling Data-OgjTk3XCUUE.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.en.vtt

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Part 15-Module 01-Lesson 06_Web Development/04. The Front End-CspuxLGFM4U.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.pt-BR.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.ar.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.es-ES.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.ja.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.pt-BR.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.zh-CN.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/05. COUNT NULLs-ngxgqfFFFLQ.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.zh-CN.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.zh-CN.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.pt-BR.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.pt-BR.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.ar.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.es-MX.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/22. L2 18 Version Control Git Commit Messages V1 V2-w1iHWpwOkMg.pt-BR.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.pt-BR.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.en.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/17. 05 Docstrings V1-_gapemxsRJY.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.it.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.zh-CN.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.en.vtt

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Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.ar.vtt

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Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.zh-CN.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.ar.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/20. The Cold Start Problem-DNz7aywJVzA.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.zh-CN.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.es-ES.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.pt-BR.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/f1.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.ar.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.ja.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.zh-CN.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/19. 15 Pipelines And Grid Search V1 V3-HZaOiSxJjCY.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.en.vtt

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Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt

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Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.pt-BR.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.pt-BR.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.ar.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.zh-CN.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c03-practicalsignificance-02.png

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.zh-CN.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.ar.vtt

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.th.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.en.vtt

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Part 06-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/03. Your First JOIN-HkX9fkNRbU8.en.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.zh-CN.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.en.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/12. 12 Pipelines And Feature Unions V1 V3-zduxy0g23L0.pt-BR.vtt

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/20. Content Based Recommendations-pnGHpB77Mys.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.en.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.ar.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.pt-BR.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/01. L1 011 Data Visualization In Data Analysis Intro V3 V3-U1VapEELBfw.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/img/codecogseqn-61.gif

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Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.zh-CN.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/27. Dummy Variables-bgxBUvPpKQQ.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.en.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. L3 081 Histograms V2-RLez9L0htGQ.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.pt-BR.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 2 Advantages Of Using Pipelines V1 V2-eT1MS3n8fZ8.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.ar.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.zh-CN.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.en-US.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/img/codecogseqn-49.gif

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/sigmoid-derivative.gif

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/10. More Personalized Recommendations-9l8mi7i6iW4.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/sigmoid-derivative.gif

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/codecogseqn-49.gif

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Part 10-Module 01-Lesson 07_Working With Remotes/06. L1 - Fetch Merge And Push-jwyQUfE1Eqw.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.it.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.zh-CN.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.en.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. L2 06 Efficient Code V1 V2-LbtxY7xetBw.en.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.zh-CN.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.en.vtt

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Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.es-ES.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.pt-BR.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/38. Bloopers Intro 1 V1-Y1weHponR2Q.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.it.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/09. Gaussian Class-TVzNdFYyJIU.pt-BR.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt1-cWB1jQgcQ1g.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/09. Gaussian Class-TVzNdFYyJIU.en.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.ar.vtt

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Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.pt-BR.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.pt-BR.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/07. Latent Factors-jZz7tFEF2Dc.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/06. Why SVD-WdW1-rRQrLk.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.zh-CN.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.pt-BR.vtt

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.es-ES.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.zh-CN.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/15. L4 151 Lesson Summary V1-5igqM44KEmw.en.vtt

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/07. Knowledge Based Recommendations-C_vU1tjQHZI.en.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.zh-CN.vtt

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Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.zh-CN.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/29. L3 21 Outro v1 V2-DStO1hBKtHQ.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.pt-BR.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/08. Know Your Audience-OjmrU5HlFD8.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.pt-BR.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/08. Know Your Audience-OjmrU5HlFD8.en.vtt

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Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.zh-CN.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.zh-CN.vtt

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/02. Disaster Relief Project Preview-DuwYAjqGM3E.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Operator-3vLGEuXAAvA.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.ar.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/25. Removing Data - Why Not-w3-5Z5mEzTM.en.vtt

2.1 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.ar.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.en.vtt

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Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.ar.vtt

2.2 kB

Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/14. 22 Cleaning Data V1 V3-zYxgkUqTX0Y.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.zh-CN.vtt

2.2 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt

2.2 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/31. 38 Outliers What To Do With Them V1 V2-Yd_fPCmGNZ0.en.vtt

2.2 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.es-ES.vtt

2.2 kB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 03 Tagging Overview-D4VdXT72ASE.ar.vtt

2.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.en.vtt

2.2 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/01. 01 Intro V1 2 V4-iW4uqhfRk10.pt-BR.vtt

2.2 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/28. T-SNE-xxcK8oZ6_WE.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.en.vtt

2.2 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/12. Combining Data From Different Sources-IfMydJvU37M.en.vtt

2.2 kB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.en.vtt

2.2 kB

Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.ar.vtt

2.2 kB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.pt-BR.vtt

2.2 kB

Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/20. 28 Missing Data Causes V1 V2-zlw8ESS6Q88.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.pt-BR.vtt

2.2 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt

2.2 kB

Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.ar.vtt

2.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.ar.vtt

2.2 kB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.pt-BR.vtt

2.2 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.zh-CN.vtt

2.2 kB

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.ar.vtt

2.2 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.en.vtt

2.2 kB

Part 15-Module 01-Lesson 06_Web Development/01. L4 Intro V2--PGMIIXFCgg.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.th.vtt

2.2 kB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/backprop-general.gif

2.2 kB

Part 04-Module 01-Lesson 01_Clustering/15. Is That The Optimal Solution-g5aPtCpBNmw.en.vtt

2.2 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.ar.vtt

2.2 kB

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.ar.vtt

2.2 kB

Part 20-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt

2.2 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt

2.2 kB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.zh-CN.vtt

2.2 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt

2.2 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.zh-CN.vtt

2.2 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/15. Momentum-r-rYz_PEWC8.zh-CN.vtt

2.2 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.en.vtt

2.2 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.ar.vtt

2.2 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.zh-CN.vtt

2.2 kB

Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.pt-BR.vtt

2.2 kB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.en.vtt

2.2 kB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.en.vtt

2.2 kB

Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt

2.2 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.en.vtt

2.2 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.zh-CN.vtt

2.2 kB

Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.zh-CN.vtt

2.2 kB

Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.en.vtt

2.2 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.en.vtt

2.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.ar.vtt

2.2 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.en.vtt

2.2 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.en.vtt

2.2 kB

Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.en.vtt

2.2 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.zh-CN.vtt

2.2 kB

Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.en.vtt

2.2 kB

Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.en.vtt

2.2 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.pt-BR.vtt

2.2 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. L3 10 Magic M V1 V3-9dEsv1aNUEE.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.ar.vtt

2.2 kB

Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.pt-BR.vtt

2.2 kB

Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.en.vtt

2.2 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt

2.2 kB

Part 20-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt

2.2 kB

Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.ar.vtt

2.2 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.ar.vtt

2.2 kB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/26. Types Of Ratings-fMjqe4sxBlQ.en.vtt

2.2 kB

Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt

2.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.pt-BR.vtt

2.2 kB

Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.pt-BR.vtt

2.2 kB

Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.en.vtt

2.3 kB

Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.zh-CN.vtt

2.3 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.en.vtt

2.3 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.en.vtt

2.3 kB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.en.vtt

2.3 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.ar.vtt

2.3 kB

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.en.vtt

2.3 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.zh-CN.vtt

2.3 kB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.en.vtt

2.3 kB

Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.zh-CN.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt

2.3 kB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/codecogseqn-2.png

2.3 kB

Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.en.vtt

2.3 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.pt-BR.vtt

2.3 kB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.en.vtt

2.3 kB

Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.pt-BR.vtt

2.3 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.ar.vtt

2.3 kB

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.ar.vtt

2.3 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.en.vtt

2.3 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.pt-BR.vtt

2.3 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.ar.vtt

2.3 kB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.pt-BR.vtt

2.3 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.ar.vtt

2.3 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.pt-BR.vtt

2.3 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.pt-BR.vtt

2.3 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt

2.3 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt

2.3 kB

Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.en.vtt

2.3 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.zh-CN.vtt

2.3 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.en.vtt

2.3 kB

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt

2.3 kB

Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.en.vtt

2.3 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt

2.3 kB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.en.vtt

2.3 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.zh-CN.vtt

2.3 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.pt-BR.vtt

2.3 kB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c03-practicalsignificance-03.png

2.3 kB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.pt-BR.vtt

2.3 kB

Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.zh-CN.vtt

2.3 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt

2.3 kB

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt

2.3 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/36. 44 Feature Engineering V1 V1-7Bof5l8xjz8.en.vtt

2.3 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.zh-CN.vtt

2.3 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.zh-CN.vtt

2.3 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.zh-CN.vtt

2.3 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt

2.3 kB

Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.ar.vtt

2.3 kB

Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt

2.3 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/22. 30 Imputing Missing Data V1 V3-A5sOJDj3AKg.en.vtt

2.3 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.en.vtt

2.3 kB

Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.ar.vtt

2.3 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.pt-BR.vtt

2.3 kB

Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.pt-BR.vtt

2.3 kB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 0424 V1-x-End5px36M.en.vtt

2.3 kB

Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt

2.3 kB

Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.en.vtt

2.3 kB

Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.en.vtt

2.3 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt

2.3 kB

Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.zh-CN.vtt

2.3 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/29. L3 21 Outro v1 V2-DStO1hBKtHQ.pt-BR.vtt

2.3 kB

Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.en.vtt

2.3 kB

Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.pt-BR.vtt

2.3 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. L3 10 Magic M V1 V3-9dEsv1aNUEE.en.vtt

2.3 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt

2.3 kB

Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.ar.vtt

2.3 kB

Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.pt-BR.vtt

2.3 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.en.vtt

2.3 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.en.vtt

2.3 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt

2.3 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt

2.3 kB

Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.en.vtt

2.3 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.en.vtt

2.3 kB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.ar.vtt

2.3 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. L4 111 Faceting V2-oUYRqI6wFGw.en.vtt

2.3 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/07. Controlling Variables-pLTneSg2MRY.en.vtt

2.3 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.en.vtt

2.3 kB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.pt-BR.vtt

2.3 kB

Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.en.vtt

2.4 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/12. Combining Data From Different Sources-IfMydJvU37M.pt-BR.vtt

2.4 kB

Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.zh-CN.vtt

2.4 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.ar.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.ja.vtt

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Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.zh-CN.vtt

2.4 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/27. Dummy Variables-bgxBUvPpKQQ.pt-BR.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/03. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.zh-CN.vtt

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/16. 04 Inline Comments V1--G6yg3Xhl8I.en.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.ar.vtt

2.4 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.ar.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.ar.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt

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Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.en.vtt

2.4 kB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/16. Identifying Recommendations-P60qvS_OTMg.en.vtt

2.4 kB

Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.en.vtt

2.4 kB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/12. SVD Practice Takeaways-2er0HUDum7k.en.vtt

2.4 kB

Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.en.vtt

2.4 kB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.pt-BR.vtt

2.4 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. L5 081 Plot Matrices V3-2wY-euTIE5g.en.vtt

2.4 kB

Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.pt-BR.vtt

2.4 kB

Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.pt-BR.vtt

2.4 kB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.pt-BR.vtt

2.4 kB

Part 07-Module 01-Lesson 01_Basic SQL/46. OR Operator-3vLGEuXAAvA.pt-BR.vtt

2.4 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.pt-BR.vtt

2.4 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.en.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.zh-CN.vtt

2.4 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

2.4 kB

Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.pt-BR.vtt

2.4 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.ar.vtt

2.4 kB

Part 20-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

2.4 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.pt-BR.vtt

2.4 kB

Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.zh-CN.vtt

2.4 kB

Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.pt-BR.vtt

2.4 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.en.vtt

2.4 kB

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.zh-CN.vtt

2.4 kB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.en-US.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.pt-BR.vtt

2.4 kB

Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.zh-CN.vtt

2.4 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.pt-BR.vtt

2.4 kB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. L2 06 Efficient Code V1 V2-LbtxY7xetBw.pt-BR.vtt

2.4 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt3-_HTolKktaC4.en.vtt

2.4 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.ar.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.zh-CN.vtt

2.4 kB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.ar.vtt

2.4 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.ar.vtt

2.4 kB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 2 Advantages Of Using Pipelines V1 V2-eT1MS3n8fZ8.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.pt-BR.vtt

2.4 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.zh-CN.vtt

2.4 kB

Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.en.vtt

2.4 kB

Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.ar.vtt

2.4 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.pt-BR.vtt

2.4 kB

Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.pt-BR.vtt

2.4 kB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/27. Goals Of Recommendation Systems-WzelOlFeDmU.en.vtt

2.4 kB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.en.vtt

2.4 kB

Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.pt-BR.vtt

2.4 kB

Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.ar.vtt

2.4 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt

2.4 kB

Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.en.vtt

2.4 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.pt-BR.vtt

2.4 kB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.en.vtt

2.4 kB

Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.pt-BR.vtt

2.4 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.en.vtt

2.5 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.pt-BR.vtt

2.5 kB

Part 15-Module 01-Lesson 06_Web Development/03. L4 Components Of A Web App V4-2aJf5sO2ox4.en.vtt

2.5 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.en.vtt

2.5 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.pt-BR.vtt

2.5 kB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.en.vtt

2.5 kB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.en.vtt

2.5 kB

Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.zh-CN.vtt

2.5 kB

Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.zh-CN.vtt

2.5 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.pt-BR.vtt

2.5 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/25. Duplicate Data-49ZwWRviAFg.en.vtt

2.5 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt

2.5 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.zh-CN.vtt

2.5 kB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.en.vtt

2.5 kB

Part 04-Module 01-Lesson 01_Clustering/15. Is That The Optimal Solution-g5aPtCpBNmw.pt-BR.vtt

2.5 kB

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/01. C4 Intro-gXlqR86h0yI.en.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/13. Early Stopping-taIJZMNwRsI.en.vtt

2.5 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/28. T-SNE-xxcK8oZ6_WE.pt-BR.vtt

2.5 kB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.en.vtt

2.5 kB

Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.ar.vtt

2.5 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.pt-BR.vtt

2.5 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.en.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.pt-BR.vtt

2.5 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

2.5 kB

Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

2.5 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

2.5 kB

Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.ar.vtt

2.5 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.en.vtt

2.5 kB

Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.ar.vtt

2.5 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.pt-BR.vtt

2.5 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.pt-BR.vtt

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.pt-BR.vtt

2.5 kB

Part 10-Module 01-Lesson 06_Undoing Changes/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L6 17 Soft Vs Medium Vs Hard Walkthrough-UN7ki2G2yKc.zh-CN.vtt

2.5 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/31. 38 Outliers What To Do With Them V1 V2-Yd_fPCmGNZ0.pt-BR.vtt

2.5 kB

Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.zh-CN.vtt

2.5 kB

Part 10-Module 01-Lesson 07_Working With Remotes/01. Intro-SBUOhyXcR1Q.zh-CN.vtt

2.5 kB

Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error-MRyxmZDngI4.en.vtt

2.5 kB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.zh-CN.vtt

2.5 kB

Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/05. COUNT NULLs-ngxgqfFFFLQ.ar.vtt

2.5 kB

Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.pt-BR.vtt

2.5 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/15. Momentum-r-rYz_PEWC8.en.vtt

2.5 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.en.vtt

2.5 kB

Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.zh-CN.vtt

2.5 kB

Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt

2.5 kB

Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.zh-CN.vtt

2.5 kB

Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt

2.5 kB

Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.pt-BR.vtt

2.5 kB

Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.pt-BR.vtt

2.5 kB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/23. Types Of Recommendations-uoXF81AO21E.en.vtt

2.5 kB

Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt

2.5 kB

Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt

2.5 kB

Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.zh-CN.vtt

2.5 kB

Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.en.vtt

2.5 kB

Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.pt-BR.vtt

2.5 kB

Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt

2.5 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.zh-CN.vtt

2.5 kB

Part 20-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt

2.5 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt

2.5 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.pt-BR.vtt

2.5 kB

Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.ar.vtt

2.5 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.en.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.en.vtt

2.5 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.zh-CN.vtt

2.5 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.pt-BR.vtt

2.5 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.zh-CN.vtt

2.5 kB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/02. Corporate Messaging Case Study-xnDsUsrF884.en.vtt

2.5 kB

Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.pt-BR.vtt

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Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.en.vtt

2.5 kB

Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.pt-BR.vtt

2.5 kB

Part 20-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt

2.5 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt

2.5 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt

2.5 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt

2.5 kB

Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.ar.vtt

2.5 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.en.vtt

2.5 kB

Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.en.vtt

2.6 kB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.en.vtt

2.6 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.th.vtt

2.6 kB

Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.ar.vtt

2.6 kB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.pt-BR.vtt

2.6 kB

Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/03. Your First JOIN-HkX9fkNRbU8.ar.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt

2.6 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.pt-BR.vtt

2.6 kB

Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.en.vtt

2.6 kB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.en.vtt

2.6 kB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.ar.vtt

2.6 kB

Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.ar.vtt

2.6 kB

Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.en.vtt

2.6 kB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.ar.vtt

2.6 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. L5 081 Plot Matrices V3-2wY-euTIE5g.pt-BR.vtt

2.6 kB

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.zh-CN.vtt

2.6 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/07. Controlling Variables-pLTneSg2MRY.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.en.vtt

2.6 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.zh-CN.vtt

2.6 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/36. 44 Feature Engineering V1 V1-7Bof5l8xjz8.pt-BR.vtt

2.6 kB

Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.ar.vtt

2.6 kB

Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt

2.6 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.en.vtt

2.6 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.zh-CN.vtt

2.6 kB

Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.zh-CN.vtt

2.6 kB

Part 14-Module 01-Lesson 01_The Data Science Process/25. Removing Data - Why Not-w3-5Z5mEzTM.pt-BR.vtt

2.6 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.en.vtt

2.6 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.zh-CN.vtt

2.6 kB

Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.ar.vtt

2.6 kB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.ar.vtt

2.6 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.ar.vtt

2.6 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.ar.vtt

2.6 kB

Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.pt-BR.vtt

2.6 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.ar.vtt

2.6 kB

Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.en.vtt

2.6 kB

Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.zh-CN.vtt

2.6 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.pt-BR.vtt

2.6 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.pt-BR.vtt

2.6 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.ar.vtt

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Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.pt-BR.vtt

2.6 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.en.vtt

2.6 kB

Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.en.vtt

2.6 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.en.vtt

2.6 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.en.vtt

2.6 kB

Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.ar.vtt

2.6 kB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/06. L1 061 Visualization In Python V1-MFS-1veFC_c.pt-BR.vtt

2.6 kB

Part 04-Module 01-Lesson 01_Clustering/08. Elbow Method For Finding K-e7fqXpo63n8.pt-BR.vtt

2.6 kB

Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.ja.vtt

2.6 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.zh-CN.vtt

2.6 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.en.vtt

2.6 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.en.vtt

2.6 kB

Part 20-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt

2.6 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.pt-BR.vtt

2.6 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.en.vtt

2.6 kB

Part 20-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.en.vtt

2.6 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/11. 06 Precision SC V1-q2wVorBfefU.pt-BR.vtt

2.6 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.en.vtt

2.6 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.en.vtt

2.6 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.zh-CN.vtt

2.6 kB

Part 16-Module 01-Lesson 02_ETL Pipelines/25. Duplicate Data-49ZwWRviAFg.pt-BR.vtt

2.6 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/15. L4 151 Lesson Summary V1-5igqM44KEmw.pt-BR.vtt

2.6 kB

Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.en.vtt

2.6 kB

Part 15-Module 01-Lesson 06_Web Development/03. L4 Components Of A Web App V4-2aJf5sO2ox4.pt-BR.vtt

2.6 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.zh-CN.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.en.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.en.vtt

2.7 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.pt-BR.vtt

2.7 kB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.pt-BR.vtt

2.7 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt

2.7 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt

2.7 kB

Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.en.vtt

2.7 kB

Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.en.vtt

2.7 kB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.zh-CN.vtt

2.7 kB

Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.ar.vtt

2.7 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.pt-BR.vtt

2.7 kB

Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.en.vtt

2.7 kB

Part 10-Module 01-Lesson 06_Undoing Changes/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L6 17 Soft Vs Medium Vs Hard Walkthrough-UN7ki2G2yKc.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.pt-BR.vtt

2.7 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.ar.vtt

2.7 kB

Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.pt-BR.vtt

2.7 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.zh-CN.vtt

2.7 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.en.vtt

2.7 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.en.vtt

2.7 kB

Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.es-ES.vtt

2.7 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.zh-CN.vtt

2.7 kB

Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.pt-BR.vtt

2.7 kB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.pt-BR.vtt

2.7 kB

Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.pt-BR.vtt

2.7 kB

Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.ar.vtt

2.7 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/11. 06 Precision SC V1-q2wVorBfefU.en.vtt

2.7 kB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.pt-BR.vtt

2.7 kB

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/01. C4 Intro-gXlqR86h0yI.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.pt-BR.vtt

2.7 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. L4 111 Faceting V2-oUYRqI6wFGw.pt-BR.vtt

2.7 kB

Part 03-Module 01-Lesson 03_Training Neural Networks/15. Momentum-r-rYz_PEWC8.pt-BR.vtt

2.7 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.pt-BR.vtt

2.7 kB

Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.en.vtt

2.7 kB

Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.en.vtt

2.7 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.zh-CN.vtt

2.7 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.zh-CN.vtt

2.7 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.en.vtt

2.7 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.pt-BR.vtt

2.7 kB

Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.en.vtt

2.7 kB

Part 20-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt

2.7 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt

2.7 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.en.vtt

2.7 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt

2.7 kB

Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.zh-CN.vtt

2.7 kB

Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.zh-CN.vtt

2.7 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.en.vtt

2.7 kB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.ar.vtt

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Part 04-Module 01-Lesson 01_Clustering/08. Elbow Method For Finding K-e7fqXpo63n8.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.ar.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.ar.vtt

2.7 kB

Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.pt-BR.vtt

2.7 kB

Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.en.vtt

2.7 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. L4 071 Box Plots V4-3gxJag12T0g.zh-CN.vtt

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Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.th.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.ja.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/06. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt

2.7 kB

Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.zh-CN.vtt

2.7 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.pt-BR.vtt

2.7 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.en.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/22. 30 Imputing Missing Data V1 V3-A5sOJDj3AKg.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.zh-CN.vtt

2.7 kB

Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.zh-CN.vtt

2.7 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.zh-CN.vtt

2.7 kB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.pt-BR.vtt

2.7 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.pt-BR.vtt

2.8 kB

Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.pt-BR.vtt

2.8 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.zh-CN.vtt

2.8 kB

Part 07-Module 01-Lesson 01_Basic SQL/06. Why Do Analysts Like SQL-uCNOtUht2Xc.zh-CN.vtt

2.8 kB

Part 20-Module 01-Lesson 02_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt

2.8 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.zh-CN.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.zh-CN.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. DataVis L5C09 V1-xlZ9AMV6VUE.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.zh-CN.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt

2.8 kB

Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.en.vtt

2.8 kB

Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.pt-BR.vtt

2.8 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/12. 07 Recall SC V1-0n5wUZiefkQ.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/08. When Accuracy Wont Work-r0-O-gIDXZ0.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/20. 02 TF-IDF-LYYWIrWbBq4.en.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L6 17 Soft Vs Medium Vs Hard Walkthrough-UN7ki2G2yKc.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/hidden-errors.gif

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.ar.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.ar.vtt

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Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.zh-CN.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/13. Ud206 017 Shell P11 - Variables-Dx3WlMZk8iA.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/08. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/01. Intro-SBUOhyXcR1Q.en.vtt

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Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/46. OR Operator-3vLGEuXAAvA.ar.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/10. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.pt-BR.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.pt-BR.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.pt-BR.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/weight-label-reference.gif

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.zh-CN.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.es-ES.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/10. Answer False Negatives And Positives-KOytJL1lvgg.pt-BR.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.pt-BR.vtt

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Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.th.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.pt-BR.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.ar.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/25. Word2Vec-7jjappzGRe0.zh-CN.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.ar.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/06. Why Do Analysts Like SQL-uCNOtUht2Xc.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.it.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.ar.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.zh-CN.vtt

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Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.zh-CN.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.ar.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.en.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.en.vtt

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Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.pt-BR.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.en.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/18. Matching Encodings-398xRMnhjGk.en.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.zh-CN.vtt

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Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.pt-BR.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.th.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.en.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.ar.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.zh-CN.vtt

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Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.en.vtt

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/backprop-error.gif

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Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.zh-CN.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.pt-BR.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.en.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/13. Ud206 017 Shell P11 - Variables-Dx3WlMZk8iA.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.en.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.pt-BR.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/05. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.ar.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.en.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/06. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.pt-BR.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.ar.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.zh-CN.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.pt-BR.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/05. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.th.vtt

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Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.ar.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.ar.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. L5 021 Non Positional Encodings For Third Variables V1-D91mm-qaDkk.pt-BR.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.zh-CN.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/08. Checking Validity-H3H1SZXqDmQ.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.es-ES.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.pt-BR.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.ar.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.ar.vtt

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Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.pt-BR.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/04. Types Of Sampling-GF_eQqNoarI.en.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.zh-CN.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. L4 031 Overplotting Transparency And Jitter 1 V4-BGqR-nxgMtg.zh-CN.vtt

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/13. Measuring SImilarity-G_Y6IPmp7Xs.en.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.it.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.pt-BR.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.zh-CN.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.pt-BR.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/33. Data Engineering Importance-VO-OrJ0JqxM.en.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt

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Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Layers-pg99FkXYK0M.zh-CN.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/02. Corporate Messaging Case Study-xnDsUsrF884.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/12. 07 Recall SC V1-0n5wUZiefkQ.en.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.zh-CN.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.ar.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/16. Shape, Size, and other Tools-fzEliHW3ZLM.zh-CN.vtt

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Part 04-Module 01-Lesson 04_PCA/08. PCA Properties-1oaaq-0wdB0.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/20. 02 TF-IDF-LYYWIrWbBq4.pt-BR.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Theory-H5JqcdIB5y0.zh-CN.vtt

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Part 06-Module 01-Lesson 07_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.en.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.zh-CN.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt

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Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.ar.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.ar.vtt

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Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/18. JOINs and Filtering-aI1kbDDNs4w.pt-BR.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.ar.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/11. SELECT FROM Statements-urOYuuav4BY.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.zh-CN.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Data Vis L4 C13 V1-Z7NjwA6jbjU.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. L4 071 Box Plots V4-3gxJag12T0g.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.zh-CN.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/04. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.ar.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.pt-BR.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual-zbvB_9f7bFs.zh-CN.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.ar.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.ar.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. L4 071 Box Plots V4-3gxJag12T0g.pt-BR.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.ar.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.en.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.en.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.pt-BR.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/33. Imputing Missing Values-CEWIPjz_gCE.en.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/29. Outliers How To Find Them-ksqzOCSAp5U.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.pt-BR.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/03. How Do We Know Our Recs Are Good-D0H_fjJ35CU.en.vtt

3.2 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.ar.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.pt-BR.vtt

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Part 04-Module 01-Lesson 04_PCA/08. PCA Properties-1oaaq-0wdB0.pt-BR.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.en.vtt

3.2 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/18. Matching Encodings-398xRMnhjGk.pt-BR.vtt

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/01. Capstone-jewlarqqbTo.en.vtt

3.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.pt-BR.vtt

3.2 kB

Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.pt-BR.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.en.vtt

3.2 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.en.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.ar.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/01. Introduction-RVcFzwBXI2M.en.vtt

3.2 kB

Part 02-Module 01-Lesson 07_Ensemble Methods/04. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt

3.2 kB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.pt-BR.vtt

3.2 kB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.pt-BR.vtt

3.2 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.pt-BR.vtt

3.2 kB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.zh-CN.vtt

3.2 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.es-MX.vtt

3.2 kB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/mse.png

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Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/18. JOINs and Filtering-aI1kbDDNs4w.zh-CN.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.en.vtt

3.2 kB

Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.ar.vtt

3.2 kB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Theory-H5JqcdIB5y0.pt-BR.vtt

3.2 kB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.zh-CN.vtt

3.2 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.en.vtt

3.2 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.pt-BR.vtt

3.2 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.pt-BR.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.en.vtt

3.2 kB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/07. Using Dummy Tests-rURTLjh3Hlc.en.vtt

3.2 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.th.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.pt-BR.vtt

3.2 kB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments-f7rzxUiHOJ0.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/06. Why Do Analysts Like SQL-uCNOtUht2Xc.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.en.vtt

3.3 kB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/07. L2 2 09 Test Driven Development DS V1 V2-M-eskssLcQM.en.vtt

3.3 kB

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.pt-BR.vtt

3.3 kB

Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.pt-BR.vtt

3.3 kB

Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/01. Starbucks Lab-QPKRboscAf4.en.vtt

3.3 kB

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.ar.vtt

3.3 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/04. Types Of Sampling-GF_eQqNoarI.pt-BR.vtt

3.3 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.pt-BR.vtt

3.3 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

3.3 kB

Part 20-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

3.3 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

3.3 kB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/08. Checking Validity-H3H1SZXqDmQ.pt-BR.vtt

3.3 kB

Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.ja.vtt

3.3 kB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.pt-BR.vtt

3.3 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt

3.3 kB

Part 20-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt

3.3 kB

Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.en.vtt

3.3 kB

Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.ar.vtt

3.3 kB

Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.ar.vtt

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Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Layers-pg99FkXYK0M.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.pt-BR.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/20. Using Grid Search-iTL43Jk9_bQ.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.zh-CN.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/33. Data Engineering Importance-VO-OrJ0JqxM.pt-BR.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.pt-BR.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-xTamXY6Z9Kg.pt-BR.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/01. What Do Data Scientists Do-sN2DbIJUZmw.en.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/01. What Do Data Scientists Do-sN2DbIJUZmw.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/01. What Do Data Scientists Do-sN2DbIJUZmw.pt-BR.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/01. What Do Data Scientists Do-sN2DbIJUZmw.pt-BR.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.zh-CN.vtt

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Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.en.vtt

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Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.pt-BR.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.pt-BR.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/04. L3 Git And Github WalkThrough V1-buMNfXkj9fg.pt-BR.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual-zbvB_9f7bFs.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.en.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments-f7rzxUiHOJ0.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/03. Next Steps-kXMCKZ4HqsM.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/11. SELECT FROM Statements-urOYuuav4BY.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/18. Feature Extraction-UgENzCmfFWE.zh-CN.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual-zbvB_9f7bFs.pt-BR.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.en.vtt

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Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.th.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-xTamXY6Z9Kg.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.zh-CN.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.ar.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.pt-BR.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

3.4 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

3.4 kB

Part 20-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.pt-BR.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.th.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution-G3fRVgLa5gI.pt-BR.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Theory-H5JqcdIB5y0.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.pt-BR.vtt

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Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.en.vtt

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Part 06-Module 01-Lesson 07_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.en.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.en.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Layers-pg99FkXYK0M.en.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.ar.vtt

3.4 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.zh-CN.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/09. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.en.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/01. Introduction-RVcFzwBXI2M.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.en.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.ar.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/05. Extract Walk Through-Bbj8rQRRVoM.pt-BR.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Unions-QmE6CMGar1U.en.vtt

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Part 15-Module 01-Lesson 06_Web Development/08. IDs and Classes-jnfDqdxDbO4.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.es-ES.vtt

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Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/25. Word2Vec-7jjappzGRe0.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. L4 031 Overplotting Transparency And Jitter 1 V4-BGqR-nxgMtg.en.vtt

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Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/12. Ud206 016 Shell P10 - Searching And Pipes-AWpVScp9z4s.pt-BR.vtt

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Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.pt-BR.vtt

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Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.pt-BR.vtt

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Build A Recommendation Engine IBM-A0rVwTbntf4.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/04. Build A Recommendation Engine IBM-A0rVwTbntf4.pt-BR.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.es-ES.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. DataVis L3 04 V2-HLum_ys7RJ0.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.it.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. L3 121 Scales And Transformations V3-PE53ga2bOME.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/01. A Repository's History - Intro-UBmg3syQS0E.zh-CN.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.it.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.ar.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/09. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.pt-BR.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.pt-BR.vtt

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/01. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.pt-BR.vtt

3.5 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.pt-BR.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/16. Shape, Size, and other Tools-fzEliHW3ZLM.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.ar.vtt

3.5 kB

Part 19-Module 01-Lesson 01_Congratulations!/index.html

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Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.th.vtt

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Part 16-Module 01-Lesson 02_ETL Pipelines/05. Extract Walk Through-Bbj8rQRRVoM.en.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.ar.vtt

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Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.en.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/03. Next Steps-kXMCKZ4HqsM.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/01. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.en.vtt

3.5 kB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.en.vtt

3.5 kB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.pt-BR.vtt

3.5 kB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.ar.vtt

3.5 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.pt-BR.vtt

3.5 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.pt-BR.vtt

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/01. Shell Intro--EtN5oD8MM0.pt-BR.vtt

3.5 kB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.ar.vtt

3.5 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.ar.vtt

3.5 kB

Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error-vLKiY0Ehors.en.vtt

3.5 kB

Part 09-Module 01-Lesson 01_Shell Workshop/12. Ud206 016 Shell P10 - Searching And Pipes-AWpVScp9z4s.zh-CN.vtt

3.5 kB

Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.th.vtt

3.5 kB

Part 01-Module 02-Lesson 02_Get Help with Your Account/index.html

3.5 kB

Part 13-Module 01-Lesson 03_Get Help with Your Account/index.html

3.5 kB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt

3.5 kB

Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.pt-BR.vtt

3.5 kB

Part 04-Module 01-Lesson 01_Clustering/13. 14 How Does KMeans Work V1-y7yZyyHgyYU.en.vtt

3.5 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.en.vtt

3.5 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.en.vtt

3.5 kB

Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution-G3fRVgLa5gI.en.vtt

3.5 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. L5 031 Color Palettes V1-nirOTWkuiSM.pt-BR.vtt

3.5 kB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 7251010 V1-sVZ5S1nnRf8.en.vtt

3.5 kB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.en.vtt

3.5 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. DataVis L5C06 V2-BzzTlWHMyV0.pt-BR.vtt

3.5 kB

Part 07-Module 01-Lesson 02_SQL Joins/18. JOINs and Filtering-aI1kbDDNs4w.en.vtt

3.5 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. L5 031 Color Palettes V1-nirOTWkuiSM.en.vtt

3.5 kB

Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.en.vtt

3.5 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.pt-BR.vtt

3.6 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.es-MX.vtt

3.6 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.en.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. L4 031 Overplotting Transparency And Jitter 1 V4-BGqR-nxgMtg.pt-BR.vtt

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Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.ar.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L6 17 Soft Vs Medium Vs Hard Walkthrough-UN7ki2G2yKc.ar.vtt

3.6 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. DataVis L5C06 V2-BzzTlWHMyV0.en.vtt

3.6 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.en.vtt

3.6 kB

Part 12-Module 01-Lesson 12_Hypothesis Testing/20. Calculating the p-value-_W3Jg7jQ8jI.pt-BR.vtt

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Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.ar.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/20. Calculating the p-value-_W3Jg7jQ8jI.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt

3.6 kB

Part 20-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt

3.6 kB

Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.en.vtt

3.6 kB

Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/index.html

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Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.en.vtt

3.6 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.en.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/26. GloVe-KK3PMIiIn8o.zh-CN.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.pt-BR.vtt

3.6 kB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt

3.6 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.zh-CN.vtt

3.6 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.pt-BR.vtt

3.6 kB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.en.vtt

3.6 kB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/07. Meet the Careers Team-cuKecPpZ7PM.en.vtt

3.6 kB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/10. Meet the Careers Team-cuKecPpZ7PM.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.ar.vtt

3.6 kB

Part 06-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.zh-CN.vtt

3.6 kB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/04. L3 Git And Github WalkThrough V1-buMNfXkj9fg.en.vtt

3.6 kB

Part 05-Module 01-Lesson 01_Congratulations!/04. Build A Recommendation Engine IBM-A0rVwTbntf4.en.vtt

3.6 kB

Part 07-Module 01-Lesson 01_Basic SQL/11. SELECT FROM Statements-urOYuuav4BY.pt-BR.vtt

3.6 kB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Build A Recommendation Engine IBM-A0rVwTbntf4.en.vtt

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Part 17-Module 04-Lesson 01_Recommendation Engines/01. IBM Project Overview-XP_f64c07Gc.en.vtt

3.6 kB

Part 04-Module 01-Lesson 01_Clustering/13. 14 How Does KMeans Work V1-y7yZyyHgyYU.pt-BR.vtt

3.7 kB

Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.pt-BR.vtt

3.7 kB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.en.vtt

3.7 kB

Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.ar.vtt

3.7 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.pt-BR.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt

3.7 kB

Part 20-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt

3.7 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt

3.7 kB

Part 13-Module 01-Lesson 02_Get Help from Peers and Mentors/index.html

3.7 kB

Part 01-Module 02-Lesson 01_Get Help from Peers and Mentors/index.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt

3.7 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.zh-CN.vtt

3.7 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. DataVis L5C03 V2-iokI7HrxeNc.en.vtt

3.7 kB

Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt

3.7 kB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/07. L2 2 09 Test Driven Development DS V1 V2-M-eskssLcQM.pt-BR.vtt

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Part 06-Module 01-Lesson 06_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.zh-CN.vtt

3.7 kB

Part 15-Module 01-Lesson 05_Portfolio Exercise Upload a Package to PyPi/index.html

3.7 kB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/index.html

3.7 kB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/index.html

3.7 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Data Vis L4 C13 V1-Z7NjwA6jbjU.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.ar.vtt

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.en.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.zh-CN.vtt

3.7 kB

Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.pt-BR.vtt

3.7 kB

Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.zh-CN.vtt

3.7 kB

Part 06-Module 01-Lesson 01_Why Python Programming/index.html

3.7 kB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.zh-CN.vtt

3.7 kB

Part 02-Module 01-Lesson 07_Ensemble Methods/08. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.en.vtt

3.7 kB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.pt-BR.vtt

3.7 kB

Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.pt-BR.vtt

3.7 kB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 0435 V1-oRhrOShUM6w.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/22. Interactions Higher Order Terms-gMHwogzqPOk.zh-CN.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/index.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 0425 V1-vPpX7ITgb3g.en.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Unions-QmE6CMGar1U.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.en.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/index.html

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.zh-CN.vtt

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/index.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/26. DATE Functions Part II-UPWkDhW4cLI.zh-CN.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/10. Meet the Careers Team-cuKecPpZ7PM.pt-BR.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Action-d3AGtKmHxUk.pt-BR.vtt

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Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.zh-CN.vtt

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Part 15-Module 01-Lesson 06_Web Development/08. IDs and Classes-jnfDqdxDbO4.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/index.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.en.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.en.vtt

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Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.ar.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.en.vtt

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Part 17-Module 04-Lesson 01_Recommendation Engines/index.html

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Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/index.html

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Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.ar.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.ar.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.ar.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.ar.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.pt-BR.vtt

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Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Figure 8 Project V2-adtlHL42AuQ.en.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/07. Square Trick-AGZEq-yQgRM.en.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.pt-BR.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/04. 06 Unit Tests V1-wb9jggHEvgI.en.vtt

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Part 06-Module 01-Lesson 06_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/04. Introduction to Blogging for Data Science-WrvGpRN5XQI.en.vtt

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Introduction to Blogging for Data Science-WrvGpRN5XQI.en.vtt

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Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/01. Blogging for Data Science-WrvGpRN5XQI.en.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.zh-CN.vtt

3.9 kB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.ar.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/09. Programming Environment Setup-EKxDnCK0NAk.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.pt-BR.vtt

3.9 kB

Part 04-Module 01-Lesson 05_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.zh-CN.vtt

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Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/index.html

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Part 10-Module 01-Lesson 07_Working With Remotes/index.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt

3.9 kB

Part 11-Module 01-Lesson 01_Introduction/index.html

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/index.html

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Part 06-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.ar.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.ar.vtt

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Part 08-Module 01-Lesson 07_Visualization Case Study/index.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c16-waffleplots1.png

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Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.ar.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.zh-CN.vtt

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Part 11-Module 01-Lesson 02_Vectors/01. Vectors 1-oPBz-MLVUHk.zh-CN.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.ar.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. DataVis L3 04 V2-HLum_ys7RJ0.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.pt-BR.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.ar.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.pt-BR.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.ar.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.en.vtt

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/._04. Possible Projects.html

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Part 01-Module 02-Lesson 02_Get Help with Your Account/._index.html

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c16-waffleplots2.png

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.ar.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/index.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Introduction to Blogging for Data Science-WrvGpRN5XQI.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/04. Introduction to Blogging for Data Science-WrvGpRN5XQI.pt-BR.vtt

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Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/01. Blogging for Data Science-WrvGpRN5XQI.pt-BR.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.pt-BR.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt

4.0 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/01. Shell Intro--EtN5oD8MM0.en.vtt

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Part 03-Module 01-Lesson 04_Keras/index.html

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Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.en.vtt

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/05. Experiment Size-sImRm8e01jA.en.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.en.vtt

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Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Figure 8 Project V2-adtlHL42AuQ.pt-BR.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/08. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.pt-BR.vtt

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Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.pt-BR.vtt

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/04. 06 Unit Tests V1-wb9jggHEvgI.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.ar.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. L3 121 Scales And Transformations V3-PE53ga2bOME.en.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/08. Grid Search SC V1-zDw-ZGiHW5I.en.vtt

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Part 12-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.ar.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.zh-CN.vtt

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/06. Creating Metrics-__7tzDUY870.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt

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Part 04-Module 01-Lesson 05_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.pt-BR.vtt

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Part 12-Module 01-Lesson 08_Python Probability Practice/index.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. Magic Methods in Code-oDuXThOqans.en.vtt

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.ar.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.ar.vtt

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/index.html

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 4271048 V1-2On65U7Panw.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.ar.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. DataVis L3 08 V2-f1we_0dUSXg.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.ar.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/19. Bag Of Words-A7M1z8yLl0w.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/01. A Repository's History - Intro-UBmg3syQS0E.pt-BR.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.ar.vtt

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Part 06-Module 01-Lesson 06_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.ar.vtt

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/09. Programming Environment Setup-EKxDnCK0NAk.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c08-histograms1.png

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Part 11-Module 01-Lesson 03_Linear Combination/index.html

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/index.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.zh-CN.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.zh-CN.vtt

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Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.pt-BR.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/08. Grid Search SC V1-zDw-ZGiHW5I.pt-BR.vtt

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c02-encodings3.png

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment Pt 2-PYzN1usi7QY.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.en.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/index.html

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Part 04-Module 01-Lesson 05_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.ar.vtt

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/index.html

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Part 20-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.pt-BR.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.en.vtt

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Action-d3AGtKmHxUk.en.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-05-22-at-12.27.22-pm.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.en-US.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.en.vtt

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Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-05-22-at-12.27.55-pm.png

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Part 05-Module 01-Lesson 01_Congratulations!/04. Figure 8 Project-QbLVh5GTuJQ.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/05. How Does MLR Work-bvM6eUYyurA.zh-CN.vtt

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/index.html

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Part 19-Module 01-Lesson 01_Congratulations!/01. Congratulations!.html

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Part 12-Module 01-Lesson 03_Admissions Case Study/index.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Figure 8 Project-QbLVh5GTuJQ.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Congratulations!/04. Figure 8 Project-QbLVh5GTuJQ.pt-BR.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.ar.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/index.html

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Part 02-Module 01-Lesson 02_Linear Regression/13. Minimizing Error Functions-RbT2TXN_6tY.en.vtt

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Part 12-Module 01-Lesson 05_Binomial Distribution/index.html

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/index.html

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Part 02-Module 01-Lesson 09_Training and Tuning/index.html

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Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/04. Experimental Design Insights With Richard Sharp-XDBw2nfOrsU.en.vtt

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.zh-CN.vtt

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.es-MX.vtt

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Part 01-Module 04-Lesson 01_What Is Ahead/02. Adam from IBM-NjjtY5UHyac.en.vtt

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 26 Branching Overview-ywcOC6CLG4s.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/26. GloVe-KK3PMIiIn8o.pt-BR.vtt

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.pt-BR.vtt

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Part 15-Module 01-Lesson 01_Introduction to Software Engineering/04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.pt-BR.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.ar.vtt

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Part 02-Module 01-Lesson 02_Linear Regression/13. Minimizing Error Functions-RbT2TXN_6tY.pt-BR.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.ar.vtt

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt

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Part 08-Module 01-Lesson 02_Design of Visualizations/16. Shape, Size, and other Tools-fzEliHW3ZLM.ar.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.ar.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/05. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.ar.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.ar.vtt

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Part 06-Module 01-Lesson 07_Pandas/index.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/19. Organizing Code Into Modules-AARS10U5bbo.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.en.vtt

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Part 04-Module 01-Lesson 04_PCA/13. 12 Interpret PCA Results V1-ZX6EACfsZbc.en.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.zh-CN.vtt

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Part 16-Module 01-Lesson 03_NLP Pipelines/19. Bag Of Words-A7M1z8yLl0w.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.ar.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.ar.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.pt-BR.vtt

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Part 04-Module 01-Lesson 01_Clustering/09. 10 KMeans In Scikit Learn V1-jkEgQLOcCGo.pt-BR.vtt

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Part 02-Module 01-Lesson 09_Training and Tuning/04. K-Fold Cross Validation.html

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/12. Lesson Conclusion.html

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Part 02-Module 01-Lesson 07_Ensemble Methods/10. Combining the Models.html

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Part 06-Module 01-Lesson 01_Why Python Programming/02. Welcome to the Course!.html

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Part 03-Module 01-Lesson 04_Keras/04. Lab Student Admissions in Keras.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.pt-BR.vtt

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/05. Project Workspace.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet Your Instructors.html

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Part 04-Module 01-Lesson 05_Random Projection and ICA/05. FastICA Algorithm.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/01. Instructor.html

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/01. Introduction.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/13. Words of Encouragement.html

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. History - A Computer Scientist's Perspective.html

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/index.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. Congratulations.html

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Part 02-Module 01-Lesson 07_Ensemble Methods/03. Random Forests.html

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/03. Gradient Descent The Math.html

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Part 14-Module 01-Lesson 01_The Data Science Process/11. How To Break Into The Field-0-Y39LZ80VE.pt-BR.vtt

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/04. Project Workspace - ETL.html

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Part 02-Module 01-Lesson 09_Training and Tuning/13. Outro.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/index.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 4381128 V1-B6bELCg6gMs.en.vtt

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.pt-BR.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/14. Other JOINs-4edRxFmWUEw.ar.vtt

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/05. Project Workspace - ML Pipeline.html

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Part 06-Module 01-Lesson 07_Pandas/09. Pandas 5 V1-lClsJnZn_7w.en.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/02. What Do Data Scientists at AirBnB Do-q7sw9vc5o1U.pt-BR.vtt

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/02. Workspace Portfolio Exercise.html

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Part 02-Module 01-Lesson 05_Naive Bayes/13. Quiz Bayes Rule .html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/09. Careers Team Content.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/26. DATE Functions Part II-UPWkDhW4cLI.ar.vtt

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Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Project Preview.html

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Part 02-Module 01-Lesson 09_Training and Tuning/01. Types of Errors.html

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. Conclusion.html

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/Project Description - Disaster Response Pipelines.html

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Lesson Conclusion.html

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Part 16-Module 01-Lesson 02_ETL Pipelines/24. Data Engineering-z6r2e_V0Td0.en.vtt

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Part 02-Module 01-Lesson 05_Naive Bayes/16. Outro.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/05. Learning Plan - First Two Weeks.html

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Part 02-Module 01-Lesson 05_Naive Bayes/01. Intro.html

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Part 02-Module 01-Lesson 05_Naive Bayes/02. Guess the Person.html

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Action-cL6ehKtJLUM.zh-CN.vtt

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Part 13-Module 01-Lesson 04_The Skills That Set You Apart/02. What Do Data Scientists at AirBnB Do-q7sw9vc5o1U.en.vtt

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Part 02-Module 01-Lesson 07_Ensemble Methods/12. More Spam Classifying.html

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Part 02-Module 01-Lesson 05_Naive Bayes/07. Solution False Positives.html

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Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent.html

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries.html

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Part 02-Module 01-Lesson 10_Finding Donors Project/01. Overview.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. Introduction.html

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/15. ROC Curve.html

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Part 06-Module 01-Lesson 03_Control Flow/index.html

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Part 06-Module 01-Lesson 01_Why Python Programming/04. Course Overview.html

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/06. Quiz Unit Tests.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/20. Outro.html

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Part 02-Module 01-Lesson 06_Support Vector Machines/04. Error Function Intuition.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/18. Lesson Summary.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.pt-BR.vtt

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/04. Learning Plan - First Two Weeks.html

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Part 04-Module 01-Lesson 04_PCA/15. 14 Interpretation Solution V1-wU2duZa0ds0.en.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/06. Classification Error.html

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Part 12-Module 01-Lesson 03_Admissions Case Study/12. Text Recap + Next Steps.html

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 26 Branching Overview-ywcOC6CLG4s.ar.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression Metrics.html

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Part 03-Module 01-Lesson 06_Image Classifier Project/03. Image Classifier - Part 1 - Development.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.en.vtt

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Part 06-Module 01-Lesson 07_Pandas/12. Pandas 7 V1-ruTYp-twXO0.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions.html

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Why Use an Elevator Pitch.html

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assets/css/fonts/KaTeX_Size2-Regular.woff

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Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion.html

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Part 02-Module 01-Lesson 06_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.en.vtt

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Part 09-Module 01-Lesson 01_Shell Workshop/01. Welcome!.html

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Part 02-Module 01-Lesson 10_Finding Donors Project/03. Starting the Project.html

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Part 02-Module 01-Lesson 05_Naive Bayes/10. Bayesian Learning 3.html

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. Code Review.html

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.en.vtt

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/14. 14 Funk SVD-H8gdwXy_npI.en.vtt

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/01. K-means considerations.html

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/12. Dog Breed Workspace.html

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Part 02-Module 01-Lesson 02_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.en.vtt

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Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability.html

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Video Introduction.html

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/10. Starbucks Project Workspace.html

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Part 01-Module 04-Lesson 01_What Is Ahead/03. Rachel from Kaggle-uVsYYzxbyIg.en.vtt

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/08. Saving and Loading Trained Networks.html

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Part 02-Module 01-Lesson 05_Naive Bayes/15. Spam Classifier - Workspace.html

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. Overview of other clustering methods.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. Introduction.html

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/08. BertelsmannArvato Project Workspace.html

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/08. Video Know Your Audience.html

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Part 02-Module 01-Lesson 04_Decision Trees/11. Quiz Do You Know Your Entropy.html

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Part 06-Module 01-Lesson 07_Pandas/14. Mini-Project Statistics From Stock Data.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Transfer Learning.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Defining Networks.html

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/11. A SMART Mnemonic for Experiment Design.html

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Part 04-Module 01-Lesson 01_Clustering/09. Screencast K-Means in Scikit Learn.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/11. Access Your Career Portal.html

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Part 06-Module 01-Lesson 06_NumPy/12. Creating ndarrays with Broadcasting.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/10. Meet the Careers Team.html

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Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph.html

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/15. DBSCAN examples applications.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. Conclusion.html

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/07. Course Structure.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.en.vtt

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Part 03-Module 01-Lesson 03_Training Neural Networks/06. Regularization.html

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Part 02-Module 01-Lesson 05_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.en.vtt

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Part 06-Module 01-Lesson 04_Functions/01. Introduction.html

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Video Self JOINs.html

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Part 09-Module 01-Lesson 01_Shell Workshop/03. Opening a terminal.html

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Part 06-Module 01-Lesson 07_Pandas/03. Why Use Pandas.html

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. HC examples and applications.html

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/Project Description - Identify Customer Segments with Arvato.html

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/07. Solutions POSITION, STRPOS, SUBSTR.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/06. World Bank API [advanced version].html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. Gaussian Distribution in One Dimension.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. Visual Example of EM Progress.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Video Introduction.html

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Create Your Elevator Pitch.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/10. Expectation Maximization Part 2.html

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Part 02-Module 01-Lesson 02_Linear Regression/06. Absolute Trick.html

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Part 02-Module 01-Lesson 02_Linear Regression/26. Regularization.html

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Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/03. Non-Linear Models.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 33514421 V1-TCaeEdrbYRc.en.vtt

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/09. Expectation Maximization Part 1.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient.html

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. World Bank Data Dashboard [advanced version].html

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Part 02-Module 01-Lesson 02_Linear Regression/09. Gradient Descent.html

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Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. Version Control in Data Science.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/11. Early Stopping.html

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Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error.html

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Part 02-Module 01-Lesson 02_Linear Regression/04. Fitting a Line Through Data.html

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Part 02-Module 01-Lesson 09_Training and Tuning/09. Grid Search in sklearn.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. Gaussian Mixture Model (GMM) Clustering.html

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Part 20-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks.html

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Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/02. Continuous Perceptrons.html

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Part 02-Module 01-Lesson 02_Linear Regression/01. Intro.html

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Part 04-Module 01-Lesson 01_Clustering/15. Video Is that the Optimal Solution.html

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Part 06-Module 01-Lesson 04_Functions/09. Quiz Documentation.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/09. Your Udacity Professional Profile.html

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Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Problems 2.html

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Part 02-Module 01-Lesson 02_Linear Regression/24. Polynomial Regression.html

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Part 04-Module 01-Lesson 01_Clustering/07. Video Changing K.html

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Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/12. Questions to Ask Yourself When Conducting a Code Review.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/09. Solution Video More On Subqueries.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/09. Local Connectivity.html

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Part 02-Module 01-Lesson 02_Linear Regression/03. Solution Housing Prices.html

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Part 02-Module 01-Lesson 06_Support Vector Machines/12. Polynomial Kernel 2.html

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Part 02-Module 01-Lesson 10_Finding Donors Project/Project Description - Finding Donors for CharityML.html

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Part 04-Module 01-Lesson 01_Clustering/13. Screencast + Text How Does K-Means Work.html

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Part 20-Module 01-Lesson 01_Neural Networks/25. Logistic Regression Algorithm.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Video Recap.html

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Part 11-Module 01-Lesson 02_Vectors/05. Transpose.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Video ROW_NUMBER RANK.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/10. Convolutional Layers (Part 1).html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/11. Quiz Optimizing - Common Books.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/13. Quiz Optimizing - Holiday Gifts.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/12. Solution Optimizing - Common Books.html

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Part 16-Module 01-Lesson 03_NLP Pipelines/23. Word Embeddings.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/08. Solution Refactoring - Wine Quality.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/14. Solution Optimizing - Holiday Gifts.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/23. Error Functions Around the World.html

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Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.pt-BR.vtt

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/14. More Advice.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/21. GMM Cluster Validation Lab.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Video Introduction to Percentiles.html

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Part 02-Module 01-Lesson 05_Naive Bayes/11. Naive Bayes Algorithm 1.html

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages.html

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Part 15-Module 01-Lesson 06_Web Development/04. The Front-End.html

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/06. Quiz POSITION, STRPOS, SUBSTR - AME DATA AS QUIZ 1.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/22. GMM Cluster Validation Lab Solution.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent.html

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/04. Arvato Terms and Conditions.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Video Aggregates in Window Functions.html

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Part 15-Module 01-Lesson 06_Web Development/19. The Web.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. Overview of The Expectation Maximization (EM) Algorithm.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings.html

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value.html

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Part 07-Module 01-Lesson 02_SQL Joins/03. Video Introduction to JOINs.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Video Introduction to Window Functions.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/10. Case Study Build Pipeline.html

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/13. Solutions CAST.html

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Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/14. Case Study Add Feature Union.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/03. Case Study Clean and Tokenize.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.en.vtt

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/21. Case Study Grid Search Pipeline.html

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Part 09-Module 01-Lesson 01_Shell Workshop/06. Current working directory (pwd).html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/17. Case Study Create Custom Transformer.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/06. Case Study Machine Learning Workflow.html

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Part 17-Module 02-Lesson 03_AB Testing Case Study/04. Building a Funnel - Discussion.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/01. Introduction.html

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Part 06-Module 01-Lesson 04_Functions/16. [Optional] Solution Iterators and Generators.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/17. External Validation Indices.html

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Part 04-Module 01-Lesson 04_PCA/10. Screencast PCA.html

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Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. Efficient Code.html

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Part 12-Module 01-Lesson 05_Binomial Distribution/17. Text Recap + Next Steps.html

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Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Video + Text Recap.html

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Part 04-Module 01-Lesson 04_PCA/17. Video When to Use PCA.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/08. Py Part 6 V1-HiTih59dCWQ.en.vtt

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Part 20-Module 01-Lesson 01_Neural Networks/20. Cross-Entropy 1.html

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Part 04-Module 01-Lesson 04_PCA/07. Video Dimensionality Reduction.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/21. 15 Making a Package v2-Hj2OBr1CGZM.pt-BR.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/08. Solutions More On Subqueries.html

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/18. Text Recap.html

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Part 12-Module 01-Lesson 13_Case Study AB tests/08. Click Through Rate.html

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Part 15-Module 01-Lesson 06_Web Development/05. 6 Screencast HTML Code V2-G7fBus1JSc0.en.vtt

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/13. Solutions UNION.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/12. Your Udacity Professional Profile.html

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Part 04-Module 01-Lesson 06_Project Identify Customer Segments/02. Project Overview.html

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Part 17-Module 02-Lesson 03_AB Testing Case Study/10. Validity, Bias, and Ethics - Discussion.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. Video What is Data Why is it important.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/09. Solutions ROW_NUMBER RANK.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running a Python Script.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/05. Counting Missing Data.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/07. On Python versions at Udacity.html

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Part 12-Module 01-Lesson 11_Confidence Intervals/12. Video Other Language Associated with Confidence Intervals.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/12. Solutions Aggregates in Window Functions.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Screencast The Cold Start Problem.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/06. Model Validation in Keras.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/02. Video Introduction to NULLs.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Screencast How Are We Doing.html

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Part 02-Module 01-Lesson 09_Training and Tuning/07. Solution Detecting Overfitting and Underfitting.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/03. Video How Do We Know Our Recommendations Are Good.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/04. Program Structure Syllabus.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/08. Notebook Knowledge Based.html

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/Project Rubric - Optimize Your GitHub Profile.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/05. Notebook MovieTweeting Data.html

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/05. Use Your Elevator Pitch on LinkedIn.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/17. Extra Swarm Plots.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/05. Text Subquery Formatting.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/26. Scikit-learn Source Code.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/18. Advanced OOP Topics.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Video Percentiles.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/03. How Computers Interpret Images.html

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Part 12-Module 01-Lesson 16_Logistic Regression/29. Screencast Model Diagnostics in Python - Part I.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/20. Using Grid Search with Pipelines.html

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Part 07-Module 01-Lesson 01_Basic SQL/30. Video Arithmetic Operators.html

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Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability.html

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Part 17-Module 04-Lesson 01_Recommendation Engines/Project Rubric - Recommendations with IBM.html

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Part 14-Module 01-Lesson 01_The Data Science Process/41. Video How to Fix This.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/11. Choosing a Plot for Discrete Data.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/03. Quiz Descriptive vs. Inferential (Udacity Students).html

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/03. Text README Showcase.html

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Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/Project Rubric - Write A Data Science Blog Post.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/08. Mini project Training an MLP on MNIST.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 1051320 V1-_4N6h82szWo.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/30. Solution Zip and Enumerate.html

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Part 06-Module 01-Lesson 05_Scripting/24. Techniques for Importing Modules.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/27. Other Things to Consider - What if Our Sample is Large.html

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Part 07-Module 01-Lesson 01_Basic SQL/45. Solutions AND and BETWEEN.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Cross-Entropy 2.html

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Part 06-Module 01-Lesson 05_Scripting/02. Python Installation.html

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Part 07-Module 01-Lesson 02_SQL Joins/09. Text + Quiz JOIN Revisited.html

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Part 12-Module 01-Lesson 11_Confidence Intervals/17. Text Recap + Next Steps.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Violin Plots.html

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Part 07-Module 01-Lesson 01_Basic SQL/41. Quiz NOT.html

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Part 12-Module 01-Lesson 08_Python Probability Practice/07. Conditional Probability Bayes Rule Quiz.html

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Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. F-beta Score.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Scatterplots and Correlation.html

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/02. Text + Images FULL OUTER JOIN.html

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Part 07-Module 01-Lesson 01_Basic SQL/34. Video LIKE.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Video + Quiz Introduction to Sampling Distributions Part I.html

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assets/css/fonts/KaTeX_Size4-Regular.ttf

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Part 14-Module 01-Lesson 01_The Data Science Process/22. Notebook + Quiz What Happened.html

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Part 14-Module 01-Lesson 01_The Data Science Process/36. Notebook + Quiz Imputing Values.html

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Part 14-Module 01-Lesson 01_The Data Science Process/29. Notebook + Quiz Removing Values.html

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Part 14-Module 01-Lesson 01_The Data Science Process/16. Notebook + Quiz Job Satisfaction.html

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Part 14-Module 01-Lesson 01_The Data Science Process/06. Quiz + Notebook A Look at the Data.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Overplotting, Transparency, and Jitter.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/07. Solution Variables and Assignment Operators.html

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Part 10-Module 01-Lesson 01_What is Version Control/04. MacLinux Setup.html

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Part 14-Module 01-Lesson 01_The Data Science Process/39. Notebook + Quiz Categorical Variables.html

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Part 14-Module 01-Lesson 01_The Data Science Process/31. Notebook + Quiz Removing Data Part II.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c17-rugplot1.png

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Part 12-Module 01-Lesson 12_Hypothesis Testing/08. Quiz Types of Errors - Part I.html

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Part 14-Module 01-Lesson 01_The Data Science Process/42. Notebook + Quiz Putting It All Together .html

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Part 14-Module 01-Lesson 01_The Data Science Process/12. Notebook + Quiz How To Break Into the Field.html

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Part 14-Module 01-Lesson 01_The Data Science Process/34. Notebook + Quiz Imputation Methods Resources.html

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Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix.html

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/09. Checking Bias.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/04. Quiz Setting Up Hypotheses.html

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Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities.html

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Part 14-Module 01-Lesson 01_The Data Science Process/05. Screencast Using Workspaces.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/28. Quiz Types of Ratings Goals of Recommendation Systems.html

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Part 07-Module 01-Lesson 01_Basic SQL/43. Video AND and BETWEEN.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. Scenario #3.html

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Part 10-Module 01-Lesson 03_Review a Repo's History/06. Viewing A Specific Commit.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Video Random Variables.html

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Part 07-Module 01-Lesson 01_Basic SQL/07. Video How Databases Store Data.html

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Part 14-Module 01-Lesson 01_The Data Science Process/38. Video Working With Categorical Variables Refresher.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c04-heatmap3.png

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/27. Putting Code on PyPi.html

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Part 14-Module 01-Lesson 01_The Data Science Process/27. Video Removing Data - Other Considerations.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/19. What is a p-value Anyway.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c07-boxplot2.png

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Part 12-Module 01-Lesson 14_Regression/19. Notebook + Quiz Regression - Your Turn - Part I.html

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Part 06-Module 01-Lesson 03_Control Flow/21. Practice While Loops.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/05. Quiz Clean Code.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/15. Solutions GROUP BY.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/14. Quiz Aliases for Multiple Window Functions.html

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/08. Creating a Slide Deck with Jupyter.html

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Part 06-Module 01-Lesson 03_Control Flow/09. Solution Boolean Expressions for Conditions.html

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Part 14-Module 01-Lesson 01_The Data Science Process/02. Video CRISP-DM.html

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Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test.html

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/04. Commit Messages.html

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Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors.html

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Part 02-Module 01-Lesson 02_Linear Regression/17. Absolute Error vs Squared Error.html

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Part 02-Module 01-Lesson 06_Support Vector Machines/08. (Optional) Margin Error Calculation.html

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Part 12-Module 01-Lesson 14_Regression/18. Notebook + Quiz How to Interpret the Results.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers.html

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Part 02-Module 01-Lesson 10_Finding Donors Project/Project Rubric - Finding Donors for CharityML.html

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/Project Rubric - Disaster Response Pipelines.html

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Part 06-Module 01-Lesson 03_Control Flow/17. Iterating Through Dictionaries with For Loops.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/28. Quiz Descriptive vs. Inferential (Udacity Students).html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c07-piecharts3.png

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Part 14-Module 01-Lesson 01_The Data Science Process/32. Removing Data Part II-lPl6-Z098Rs.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/19. Quiz ORDER BY.html

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Part 07-Module 01-Lesson 01_Basic SQL/46. Video OR.html

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Part 20-Module 01-Lesson 01_Neural Networks/10. Perceptron Trick.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/31. Quiz CASE.html

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Part 14-Module 01-Lesson 01_The Data Science Process/45. Video The Data Science Process - Evaluate Deploy.html

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/img/screen-shot-2017-10-27-at-1.49.58-pm.png

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2017-10-27-at-1.49.58-pm.png

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Video Capital vs. Lower.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/11. Quiz Booleans, Comparison Operators, and Logical Operators.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/20. Video Predicting Salary.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/03. Levels of Measurement Types of Data.html

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Part 07-Module 01-Lesson 01_Basic SQL/10. Statements.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. Advanced API Code Walk-through-AkqO534YooE.en.vtt

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Part 06-Module 01-Lesson 05_Scripting/20. Importing Local Scripts.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. Plot Matrices.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/10. Booleans, Comparison Operators, and Logical Operators.html

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/07. Text Medium Getting Started Post and Links.html

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/03. Testing your models.html

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Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/21. Scenario #1.html

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Part 06-Module 01-Lesson 03_Control Flow/32. Quiz List Comprehensions.html

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Part 14-Module 01-Lesson 01_The Data Science Process/30. ScreenCast Removing Data Solution.html

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Part 14-Module 01-Lesson 01_The Data Science Process/17. Screencast Job Satisfaction.html

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Part 14-Module 01-Lesson 01_The Data Science Process/23. Screencast What Happened Solution.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/28. Solutions DATE Functions.html

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Part 14-Module 01-Lesson 01_The Data Science Process/07. Screencast A Look at the Data.html

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Part 14-Module 01-Lesson 01_The Data Science Process/37. Screencast Imputing Values Solution.html

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Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/27. Quiz Summation.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c08-plotmatrices3.png

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Part 14-Module 01-Lesson 01_The Data Science Process/24. Video Working With Missing Values.html

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Part 14-Module 01-Lesson 01_The Data Science Process/40. Screencast Categorical Variables Solution.html

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Part 14-Module 01-Lesson 01_The Data Science Process/32. Screencast Removing Data Part II Solution.html

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Part 07-Module 01-Lesson 01_Basic SQL/31. Quiz Arithmetic Operators.html

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Part 14-Module 01-Lesson 01_The Data Science Process/35. Screencast Imputation Methods Resources Solution.html

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Part 14-Module 01-Lesson 01_The Data Science Process/25. Video Removing Data - Why Not.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/29. Video CASE Statements.html

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Part 03-Module 01-Lesson 04_Keras/03. Pre-Lab Student Admissions in Keras.html

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Part 15-Module 01-Lesson 06_Web Development/03. Components of a Web App.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Video Summation.html

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Part 14-Module 01-Lesson 01_The Data Science Process/09. Video Business Data Understanding .html

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Part 14-Module 01-Lesson 01_The Data Science Process/43. Screencast + Notebook Putting It All Together Solution.html

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Part 14-Module 01-Lesson 01_The Data Science Process/26. Video Removing Data - When Is It OK.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/23. HAVING.html

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Part 07-Module 01-Lesson 01_Basic SQL/22. Quiz ORDER BY Part II.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/12. Video + Quiz Collaborative Filtering Content Based Recs.html

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Part 14-Module 01-Lesson 01_The Data Science Process/13. Screencast How to Break Into the Field Solution.html

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/24. Text Interpreting Interactions.html

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Part 14-Module 01-Lesson 01_The Data Science Process/03. Video The Data Science Process - Business Data.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/12. Launching the notebook server.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/07. Identifying Data Types.html

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assets/css/fonts/KaTeX_Caligraphic-Regular.woff

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Backpropagation.html

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Part 15-Module 01-Lesson 06_Web Development/16. 18 Screencast Plotly V2-QsmOW1jNeio.en.vtt

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Part 12-Module 01-Lesson 07_Bayes Rule/34. Learning from Sensor Data.html

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. Advanced API Code Walk-through-AkqO534YooE.pt-BR.vtt

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c09-clusteredbar3.png

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Part 14-Module 01-Lesson 01_The Data Science Process/07. A Look at the Data-vPHVUYvCNGE.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/10. Video Gathering Wrangling.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/23. Quiz Introduction to Notation.html

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Part 06-Module 01-Lesson 06_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.en.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/31. Dictionaries and Identity Operators.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c16-qqplot2.png

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Part 20-Module 01-Lesson 02_Deep Neural Networks/27. Pre-Lab IMDB Data in Keras.html

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Part 16-Module 01-Lesson 02_ETL Pipelines/01. Introduction.html

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Part 10-Module 01-Lesson 07_Working With Remotes/02. Remote Repositories.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/04. What is Anaconda.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Video Notation for Parameters vs. Statistics.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/22. Quiz Calculating a p-value.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/16. Text Measures of Center and Spread Summary.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. Absolute vs. Relative Frequency.html

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Part 06-Module 01-Lesson 03_Control Flow/25. Break, Continue.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c09-clusteredbar5.png

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/06. Creating Metrics.html

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assets/css/fonts/KaTeX_Caligraphic-Bold.woff

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Part 20-Module 01-Lesson 01_Neural Networks/16. Softmax.html

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Part 14-Module 01-Lesson 01_The Data Science Process/33. Video Imputing Missing Values.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 11123244 V1-QlILlYuWF9U.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/35. Imputation Methods-OwEWSBitF-Q.en.vtt

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/15. Solutions WITH.html

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Part 07-Module 01-Lesson 03_SQL Aggregations/24. Solutions HAVING.html

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Part 12-Module 01-Lesson 14_Regression/img/screen-shot-2017-11-10-at-2.43.00-pm.png

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/08. Using Pipeline.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/13. Text SVD Closed Form Solution.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/13. Convolutional Layers in Keras.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/30. Quiz Sets.html

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Part 06-Module 01-Lesson 05_Scripting/12. Errors and Exceptions.html

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/03. Project Details.html

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assets/css/fonts/KaTeX_Script-Regular.woff2

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Part 04-Module 01-Lesson 04_PCA/05. Latent Features.html

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Part 12-Module 01-Lesson 08_Python Probability Practice/06. Cancer Test Results.html

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Part 14-Module 01-Lesson 01_The Data Science Process/08. What Should You Check.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/13. Quiz Notation.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Faceting.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/14. Video FunkSVD.html

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Part 14-Module 01-Lesson 01_The Data Science Process/19. Video The Data Science Process - Modeling.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/25. Quiz Recommendation Methods.html

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Part 06-Module 01-Lesson 03_Control Flow/05. Quiz Conditional Statements.html

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assets/css/fonts/KaTeX_Size2-Regular.ttf

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Part 07-Module 01-Lesson 02_SQL Joins/04. Text + Quiz Your First JOIN.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/24. Notebook + Quiz Bootstrapping.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/13. Strings.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/09. Quiz Integers and Floats.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 01725 V1-UFmfDAiaOmw.en.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/05. Text Map of SQL Content.html

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Part 16-Module 01-Lesson 02_ETL Pipelines/02. Lesson Overview.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/10. Text Introduction to the Standard Deviation and Variance.html

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Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Neural Network Architecture.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Trick.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt

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Part 12-Module 01-Lesson 14_Regression/20. Notebook + Quiz Your Turn - Part II.html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/03. Program Structure Schedule.html

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/12. Linear Transformation Quiz Answers.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c11-faceting3.png

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Part 07-Module 01-Lesson 01_Basic SQL/03. Video + Text The Parch Posey Database.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/19. Notebook + Quiz Central Limit Theorem - Part II.html

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Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Union.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/19. Quiz Shape and Outliers (What's the Impact).html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. Box Plots.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color8.png

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/18. Notebook + Quiz Central Limit Theorem.html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/24. Text More Recommendation Technniques.html

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/12. Bad Visual Quizzes (Part I).html

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Part 03-Module 01-Lesson 06_Image Classifier Project/Project Rubric - Create Your Own Image Classifier.html

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Part 12-Module 01-Lesson 14_Regression/09. Correlation Coefficient Quizzes.html

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Part 07-Module 01-Lesson 01_Basic SQL/04. Quiz ERD Fundamentals.html

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Part 07-Module 01-Lesson 02_SQL Joins/19. Quiz Last Check.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. Pie Charts.html

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Part 07-Module 01-Lesson 01_Basic SQL/16. Quiz LIMIT.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/13. Advanced Standard Deviation and Variance.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/09. Video Singular Value Decomposition.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. Class, Object, Method and Attribute.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c16-qqplot3.png

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Part 07-Module 01-Lesson 01_Basic SQL/49. Text Recap Looking Ahead.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/03. Quiz Arithmetic Operators.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c06-adaptations2.png

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/12. Quizzes UNION.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/26. Text Descriptive Statistics Summary .html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/07. Quiz More On Subqueries.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/13. Bad Visual Quizzes (Part II).html

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Part 06-Module 01-Lesson 02_Data Types and Operators/08. Integers and Floats.html

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Part 07-Module 01-Lesson 01_Basic SQL/08. Text + Quiz Types of Databases.html

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Part 02-Module 01-Lesson 04_Decision Trees/17. Hyperparameters.html

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.en.vtt

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/21. Notebook + Quiz Central Limit Theorem - Part III.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/20. String Methods.html

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Part 14-Module 01-Lesson 01_The Data Science Process/46. Text Recap.html

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/02. Motivation for Data Visualization.html

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Part 14-Module 01-Lesson 01_The Data Science Process/18. Video It Is Not Always About ML.html

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/10. Dummy Variables.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Softmax.html

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/05. Quiz Regression Metrics.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. Procedural vs. Object-Oriented Programming.html

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Part 15-Module 01-Lesson 05_Portfolio Exercise Upload a Package to PyPi/01. Introduction.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Py Part 8 V1-3eqn5sgCOsY.en.vtt

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assets/css/fonts/KaTeX_Size1-Regular.ttf

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/02. Applications of CNNs.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/25. List Methods.html

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Part 06-Module 01-Lesson 03_Control Flow/15. Quiz Match Inputs To Outputs.html

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Algorithm.html

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Neural Network Architecture.html

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/backprop-network.png

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Part 07-Module 01-Lesson 03_SQL Aggregations/32. Solutions CASE.html

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Part 15-Module 01-Lesson 06_Web Development/05. HTML.html

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/14. [Optional] Notebook + Quiz Other Encodings.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods.html

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Part 10-Module 01-Lesson 03_Review a Repo's History/03. Changing How Git Log Displays Information.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. What are Jupyter notebooks.html

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/06. Having Git Ignore Files.html

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Part 02-Module 01-Lesson 02_Linear Regression/25. Quiz Polynomial Regression.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. Other Adaptations of Bivariate Plots.html

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Part 06-Module 01-Lesson 06_NumPy/09. Boolean Indexing, Set Operations, and Sorting.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/03. [For Windows] Configuring Git Bash to Run Python.html

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Part 07-Module 01-Lesson 02_SQL Joins/20. Solutions Last Check.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/05. Text + Quiz Data Types (Ordinal vs. Nominal).html

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Part 10-Module 01-Lesson 03_Review a Repo's History/04. Viewing Modified Files.html

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Part 15-Module 01-Lesson 06_Web Development/20. Flask.html

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Part 06-Module 01-Lesson 06_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.pt-BR.vtt

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Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/01. Introduction.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/10. Quiz Types of Errors - Part II(a).html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.pt-BR.vtt

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Part 07-Module 01-Lesson 01_Basic SQL/14. Formatting Best Practices.html

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Part 11-Module 01-Lesson 03_Linear Combination/06. Solving a Simplified Set of Equations.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c03-barchart5.png

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Part 07-Module 01-Lesson 02_SQL Joins/17. Solutions LEFT and RIGHT JOIN .html

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 03362 V1-MwRSg5RASoc.en.vtt

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Part 07-Module 01-Lesson 02_SQL Joins/08. Quiz Primary - Foreign Key Relationship.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Py Part 8 V1-3eqn5sgCOsY.pt-BR.vtt

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Part 06-Module 01-Lesson 07_Pandas/06. Arithmetic Operations on Pandas Series.html

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Part 06-Module 01-Lesson 03_Control Flow/16. Building Dictionaries.html

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Part 15-Module 01-Lesson 06_Web Development/30. Deployment-YPfNzpnm_Rk.pt-BR.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/08. Pre-Lab Student Admissions in Keras.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Heat Maps.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/28. Quiz Tuples.html

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assets/css/fonts/KaTeX_SansSerif-Regular.woff2

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c08-multimetrics-02.png

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/19. Notebook + Quiz Multicollinearity VIFs.html

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Part 06-Module 01-Lesson 07_Pandas/05. Accessing and Deleting Elements in Pandas Series.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/34. Quiz More With Dictionaries.html

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Part 18-Module 01-Lesson 01_Data Scientist Capstone/Project Rubric - Capstone Project.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/15. Homework 1 Final Quiz on Measures Spread.html

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/05. Quizzes on Data Story Telling.html

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Part 15-Module 01-Lesson 06_Web Development/30. Deployment-YPfNzpnm_Rk.en.vtt

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Part 10-Module 01-Lesson 01_What is Version Control/03. Git and Version Control Terminology.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments.html

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Part 02-Module 01-Lesson 02_Linear Regression/20. Multiple Linear Regression.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.zh-CN.vtt

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Part 06-Module 01-Lesson 03_Control Flow/18. Quiz Iterating Through Dictionaries.html

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Part 06-Module 01-Lesson 05_Scripting/28. Online Resources.html

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Part 05-Module 01-Lesson 01_Congratulations!/img/screen-shot-2018-07-05-at-7.30.12-pm.png

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/28. Quiz Notation for the Mean.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/04. Quiz Data Types (Quantitative vs. Categorical).html

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Part 06-Module 01-Lesson 02_Data Types and Operators/06. Quiz Variables and Assignment Operators.html

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Part 12-Module 01-Lesson 16_Logistic Regression/30. Notebook + Quiz Model Diagnostics.html

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-14-at-10.03.16-am.png

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Part 16-Module 01-Lesson 02_ETL Pipelines/03. World Bank Datasets.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/21. Quiz What is a p-value Anyway.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Adaptation of Univariate Plots.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c08-histograms4.png

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Part 06-Module 01-Lesson 03_Control Flow/10. For Loops.html

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Part 07-Module 01-Lesson 01_Basic SQL/06. Video Why SQL.html

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Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1.html

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Part 06-Module 01-Lesson 03_Control Flow/29. Quiz Zip and Enumerate.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c03-barchart6.png

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Part 10-Module 01-Lesson 07_Working With Remotes/04. Push Changes To A Remote.html

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/16. [Optional] Text Linear Model Assumptions.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/23. Quiz Lists and Membership Operators.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/24. Visualizing CNNs (Part 2).html

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Part 10-Module 01-Lesson 07_Working With Remotes/05. Pulling Changes From A Remote.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. Bar Charts.html

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Part 02-Module 01-Lesson 02_Linear Regression/16. Quiz Mini-Batch Gradient Descent.html

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Part 14-Module 01-Lesson 01_The Data Science Process/13. How to Break Into the Field Solution-Db_2Lmwo4EY.pt-BR.vtt

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/14. Quiz Applied Standard Deviation and Variance.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c13-lineplot2.png

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Part 06-Module 01-Lesson 05_Scripting/04. [For Windows] Configuring Git Bash to Run Python.html

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. Polishing Plots.html

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Part 12-Module 01-Lesson 14_Regression/11. Quiz What Defines A Line - Notation Quiz.html

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Part 12-Module 01-Lesson 16_Logistic Regression/05. Notebook + Quiz Fitting Logistic Regression in Python.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.zh-CN.vtt

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Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables and Assignment Operators.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/26. Notebook + Quiz Drawing Conclusions.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/24. Quiz Shape and Outliers (Visuals).html

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Part 14-Module 01-Lesson 01_The Data Science Process/40. Categorical Variables-p3gDUkBD9uM.pt-BR.vtt

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/06. A Couple of Notes about OOP.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/20. Extra Ridgeline Plots.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Video + Quiz Write Your First Subquery.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/39. Summary.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step1-cd.png

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Part 06-Module 01-Lesson 05_Scripting/img/step1-cd.png

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/10. Text + Quiz Data Types (Ordinal vs. Nominal).html

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Part 12-Module 01-Lesson 13_Case Study AB tests/17. Quiz Difficulties in AB Testing.html

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Part 06-Module 01-Lesson 04_Functions/02. Defining Functions.html

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Part 10-Module 01-Lesson 02_Create A Git Repo/02. Create A Repo From Scratch.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/img/lag.png

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assets/css/fonts/KaTeX_SansSerif-Italic.woff2

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Part 02-Module 01-Lesson 02_Linear Regression/22. (Optional) Closed form Solution Math.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c09b-subplotsa.png

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Part 20-Module 01-Lesson 01_Neural Networks/11. Perceptron Algorithm.html

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Part 14-Module 01-Lesson 01_The Data Science Process/21. Predicting Salary-HTp4LA1MJh8.pt-BR.vtt

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Part 02-Module 01-Lesson 06_Support Vector Machines/17. SVMs in sklearn.html

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/09. A Gaussian Class.html

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics in Experimentation.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/09. Notebook + Quiz Sampling Distributions Python.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/13. Quiz Types of Errors - Part III.html

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Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/27. Quiz + Text Recap Next Steps.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. Non-Positional Encodings for Third Variables.html

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Part 15-Module 01-Lesson 06_Web Development/12. JavaScript.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/18. Measures of Center (Mode).html

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Part 06-Module 01-Lesson 02_Data Types and Operators/36. Quiz Compound Data Structures.html

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Part 14-Module 01-Lesson 01_The Data Science Process/21. Predicting Salary-HTp4LA1MJh8.en.vtt

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Part 06-Module 01-Lesson 03_Control Flow/07. Boolean Expressions for Conditions.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/25. Quiz Connecting Errors and P-Values.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step5-source.png

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Part 06-Module 01-Lesson 05_Scripting/img/step5-source.png

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/img/lead-3.png

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Part 14-Module 01-Lesson 01_The Data Science Process/40. Categorical Variables-p3gDUkBD9uM.en.vtt

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Part 14-Module 01-Lesson 01_The Data Science Process/13. How to Break Into the Field Solution-Db_2Lmwo4EY.en.vtt

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. Scales and Transformations.html

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/19. Text Recap.html

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Part 12-Module 01-Lesson 16_Logistic Regression/08. Notebook + Quiz Interpret Results.html

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. Squash Commits.html

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Part 07-Module 01-Lesson 01_Basic SQL/12. Your First Queries in SQL Workspace.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/25. Quiz Shape and Outliers (Final Quiz).html

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Part 06-Module 01-Lesson 02_Data Types and Operators/22. Lists and Membership Operators.html

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Part 20-Module 01-Lesson 01_Neural Networks/24. Gradient Descent.html

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/06. Quiz Data Types (Quantitative vs. Categorical).html

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-05-25-at-11.22.02-am.png

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Part 03-Module 01-Lesson 04_Keras/02. Keras.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/23. Quiz Shape and Outliers (Comparing Distributions).html

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assets/css/fonts/KaTeX_SansSerif-Bold.woff2

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Part 06-Module 01-Lesson 02_Data Types and Operators/14. Quiz Strings.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/15. Quiz More Hypothesis Testing Practice.html

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Part 12-Module 01-Lesson 11_Confidence Intervals/img/screen-shot-2017-11-06-at-1.14.05-pm.png

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Part 12-Module 01-Lesson 13_Case Study AB tests/06. Quiz Experiment I.html

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/Project Rubric - Improve Your LinkedIn Profile.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/17. Quiz Type and Type Conversion.html

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Part 12-Module 01-Lesson 14_Regression/07. Quizzes On Scatter Plots.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/img/iris-box-plot.png

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Part 02-Module 01-Lesson 04_Decision Trees/18. Decision Trees in sklearn.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c16-waffleplots3.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Algorithm.html

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking A Repository.html

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Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.ar.vtt

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Part 10-Module 01-Lesson 02_Create A Git Repo/04. Determine A Repo's Status.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. Histograms.html

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Part 06-Module 01-Lesson 02_Data Types and Operators/32. Quiz Dictionaries and Identity Operators.html

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Part 12-Module 01-Lesson 11_Confidence Intervals/06. Notebook + Quiz Difference in Means.html

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Part 15-Module 01-Lesson 06_Web Development/10. CSS.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/19. Extra Stacked Plots.html

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Part 03-Module 01-Lesson 06_Image Classifier Project/02. Introduction to GPU Workspaces.html

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Part 10-Module 01-Lesson 02_Create A Git Repo/03. Clone An Existing Repo.html

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Part 12-Module 01-Lesson 11_Confidence Intervals/04. Notebook + Quiz Building Confidence Intervals.html

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Part 06-Module 01-Lesson 06_NumPy/08. Slicing ndarrays.html

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Part 06-Module 01-Lesson 03_Control Flow/13. Quiz For Loops.html

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Part 14-Module 01-Lesson 01_The Data Science Process/28. Quiz Removing Data.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/14. Quiz Dimensionality.html

16 kB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent.html

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Part 15-Module 01-Lesson 06_Web Development/14. Bootstrap Library-KsrqjguHWUI.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/img/step2-pwd.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step2-pwd.png

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assets/css/fonts/KaTeX_SansSerif-Regular.woff

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Part 12-Module 01-Lesson 12_Hypothesis Testing/33. Quiz + Text Recap.html

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Part 06-Module 01-Lesson 03_Control Flow/02. Conditional Statements.html

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Part 12-Module 01-Lesson 07_Bayes Rule/36. Learning Objectives - Bayes' Rule.html

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Part 10-Module 01-Lesson 03_Review a Repo's History/05. Viewing File Changes.html

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Part 06-Module 01-Lesson 05_Scripting/22. Quiz The Standard Library.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/11. Quiz Aggregates in Window Functions.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/06. Quiz Setting Up Hypothesis Tests.html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Scripting/18. Quiz Reading and Writing Files.html

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Part 06-Module 01-Lesson 03_Control Flow/08. Quiz Boolean Expressions for Conditions.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/17. Extra Waffle Plots.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c02-scatterplot2.png

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Part 06-Module 01-Lesson 06_NumPy/07. Accessing, Deleting, and Inserting Elements Into ndarrays.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/img/lag-diff.png

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Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/img/c08-multimetrics-01.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Line Plots.html

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Part 15-Module 01-Lesson 06_Web Development/30. Deployment.html

17 kB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Merging.html

17 kB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/04. Quiz Descriptive vs. Inferential (Bagels).html

17 kB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/speaking.png

17 kB

assets/css/fonts/KaTeX_Typewriter-Regular.woff2

17 kB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/07. Quiz Interpreting Coefficients in MLR.html

17 kB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. Perceptrons as Logical Operators.html

17 kB

Part 06-Module 01-Lesson 06_NumPy/11. Arithmetic operations and Broadcasting.html

17 kB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Solution Subquery Mania.html

17 kB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c05-faceting2.png

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Part 02-Module 01-Lesson 02_Linear Regression/18. Linear Regression in scikit-learn.html

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/29. Quiz Descriptive vs. Inferential (Bagels).html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.en.vtt

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/25. Notebook + Quiz Interpreting Model Coefficients.html

18 kB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.pt-BR.vtt

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Part 12-Module 01-Lesson 03_Admissions Case Study/13. Case Study in Python.html

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assets/css/fonts/KaTeX_SansSerif-Italic.woff

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.en.vtt

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Part 20-Module 01-Lesson 02_Deep Neural Networks/07. Keras.html

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Part 07-Module 01-Lesson 02_SQL Joins/16. LEFT and RIGHT JOIN.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/31. Notebook + Quiz Other Things to Consider.html

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Part 06-Module 01-Lesson 05_Scripting/img/step4-alias.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step4-alias.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/16. Extra Q-Q Plots.html

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Part 12-Module 01-Lesson 07_Bayes Rule/31. Learning Objectives - Conditional Probability.html

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Part 06-Module 01-Lesson 06_NumPy/04. Creating and Saving NumPy ndarrays.html

18 kB

Part 15-Module 01-Lesson 06_Web Development/14. Bootstrap Library-KsrqjguHWUI.en.vtt

18 kB

Part 14-Module 01-Lesson 01_The Data Science Process/15. Quiz Bootcamp Takeaways.html

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Part 12-Module 01-Lesson 14_Regression/img/screen-shot-2017-08-28-at-1.04.03-pm.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c02-scatterplot3.png

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Part 16-Module 01-Lesson 02_ETL Pipelines/05. Extract.html

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Part 02-Module 01-Lesson 09_Training and Tuning/06. Detecting Overfitting and Underfitting with Learning Curves.html

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/02. Tagging.html

18 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c06-violinplot2.png

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/12. Notebook + Quiz Dummy Variables.html

18 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c13-lineplot3.png

18 kB

assets/css/fonts/KaTeX_Caligraphic-Regular.ttf

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/10. Figures, Axes, and Subplots.html

18 kB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Review a Repo's History/02. Displaying A Repository's Commits.html

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/04. Notebook + Quiz Fitting A MLR Model.html

19 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c06-violinplot1.png

19 kB

assets/css/fonts/KaTeX_SansSerif-Bold.woff

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Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-02-21-at-6.41.35-pm.png

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/25. Transfer Learning.html

19 kB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c07-boxplot3.png

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What is an Experiment.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. Color Palettes.html

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Part 02-Module 01-Lesson 02_Linear Regression/27. Quiz Regularization.html

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c07-piecharts2.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Branching.html

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Part 12-Module 01-Lesson 12_Hypothesis Testing/18. Notebook + Quiz Simulating from the Null.html

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/media/unnamed-project-desc-1.gif

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Part 20-Module 01-Lesson 01_Neural Networks/08. Perceptrons as Logical Operators.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c03-overplotting3.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation.html

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Part 17-Module 02-Lesson 03_AB Testing Case Study/06. Deciding on Metrics - Part II.html

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. Stay in sync with source project.html

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Part 14-Module 01-Lesson 01_The Data Science Process/44. Text + Quiz Results.html

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/06. Merge Conflicts.html

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assets/css/fonts/KaTeX_Fraktur-Bold.woff2

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. Perceptrons as Logical Operators.html

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/03. Reviewing Existing Work.html

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assets/css/fonts/KaTeX_Typewriter-Regular.woff

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/student-acceptance.png

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Part 03-Module 01-Lesson 04_Keras/img/student-acceptance.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/02. Git Add.html

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step3-path.png

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Part 06-Module 01-Lesson 05_Scripting/img/step3-path.png

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Part 12-Module 01-Lesson 12_Hypothesis Testing/30. Notebook + Quiz Other Things to Consider.html

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer Perceptrons.html

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Part 12-Module 01-Lesson 14_Regression/img/screen-shot-2017-08-28-at-1.47.06-pm.png

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/04. Determining What To Work On.html

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/challenger-good.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/08. Implementing Backpropagation.html

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/03. Git Commit.html

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Video Comparing a Row to Previous Row.html

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Part 06-Module 01-Lesson 07_Pandas/08. Creating Pandas DataFrames.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c19-stackedbars1.png

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Part 12-Module 01-Lesson 12_Hypothesis Testing/11. Quiz Types of Errors - Part II(b).html

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.pt-BR.vtt

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/img/lead-diff.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c07-boxplot1.png

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Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.en.vtt

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Part 10-Module 01-Lesson 06_Undoing Changes/04. Resetting Commits.html

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assets/css/fonts/KaTeX_Math-Italic.woff

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Part 02-Module 01-Lesson 02_Linear Regression/img/quadraticlinearregression.png

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/img/quadraticlinearregression.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c06-violinplot3.png

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Part 02-Module 01-Lesson 02_Linear Regression/28. Feature Scaling.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c06-adaptations5.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/just-a-simple-lin-reg.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/05. Implementing Gradient Descent.html

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/img/gmm-1d-quiz.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c04-heatmap1.png

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c16-waffleplotsa.png

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Part 11-Module 01-Lesson 01_Introduction/img/img-4646.jpg

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c20-ridgeline2.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/lin-reg-w-outliers.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/04. Branching Effectively.html

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c17-rugplot2.png

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Part 12-Module 01-Lesson 14_Regression/img/screen-shot-2017-08-28-at-2.22.27-pm.png

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c02-encodings2.png

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/img/l6-c06-polishing1.png

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Part 06-Module 01-Lesson 06_NumPy/05. Using Built-in Functions to Create ndarrays.html

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color2.png

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/conv-dims.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/lin-reg-no-outliers.png

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c03-color7.png

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/pooling-dims.png

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Part 06-Module 01-Lesson 07_Pandas/09. Accessing Elements in Pandas DataFrames.html

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assets/css/fonts/KaTeX_SansSerif-Regular.ttf

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assets/css/fonts/KaTeX_Main-Bold.woff2

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-components.png

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/img/screen-shot-2017-09-03-at-6.12.14-pm.png

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-03-28-at-4.44.34-pm.png

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-07-27-at-1.24.38-pm.png

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/img/screen-shot-2018-07-27-at-1.24.38-pm.png

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/img/screen-shot-2017-09-03-at-6.34.02-pm.png

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Part 10-Module 01-Lesson 07_Working With Remotes/03. Add A Remote Repository.html

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-03-28-at-5.11.09-pm.png

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assets/css/fonts/KaTeX_Main-Regular.woff2

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-03-28-at-5.15.59-pm.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c13-lineplot5.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c12-adaptations3.png

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Part 06-Module 01-Lesson 07_Pandas/10. Dealing with NaN.html

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assets/css/fonts/KaTeX_Fraktur-Regular.ttf

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-14-at-10.05.37-am.png

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Part 06-Module 01-Lesson 07_Pandas/12. Loading Data into a Pandas DataFrame.html

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/grid-layer-1.png

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/img/histogram-nonnormal.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/screen-shot-2018-05-26-at-4.45.34-pm.png

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-06-07-at-12.02.10-pm.png

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assets/css/fonts/KaTeX_Main-Bold.woff

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/img/l3-c15-kde1.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/screen-shot-2018-05-26-at-4.59.04-pm.png

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c08-plotmatrices1.png

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/maxpool.jpeg

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/img/udacitylogo-copy.png

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/udacitylogo-copy.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/local-minima.png

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Part 12-Module 01-Lesson 14_Regression/img/1200px-linear-regression.svg.png

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/img/anscombe-table.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c20-ridgeline3.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-02-local-and-remote-repos.png

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assets/css/fonts/KaTeX_AMS-Regular.woff

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-environments.png

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assets/css/fonts/KaTeX_Math-Italic.ttf

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c13-lineplot1.png

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Part 10-Module 01-Lesson 01_What is Version Control/img/ud123-l1-terminal-config-mac.png

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assets/css/jquery.mCustomScrollbar.min.css

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/screen-shot-2018-05-26-at-4.40.07-pm.png

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Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-23-at-10.49.16-am.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-02-multiple-remote-repos.png

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-26-at-11.48.02-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/step6-testrun.png

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Part 06-Module 01-Lesson 05_Scripting/img/step6-testrun.png

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assets/css/fonts/KaTeX_Main-BoldItalic.ttf

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-26-at-11.21.42-pm.png

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assets/js/jquery.mCustomScrollbar.concat.min.js

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-26-at-11.16.45-pm.png

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-26-at-10.16.48-pm.png

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/img/screen-shot-2018-08-27-at-3.50.29-pm.png

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-14-at-10.08.56-am.png

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c18-swarmplot1.png

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/layer-1-grid.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-11-at-11.54.30-am.png

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assets/css/fonts/KaTeX_Main-Italic.ttf

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Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/img/l4-c20-ridgeline1.png

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-terminal.png

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Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/img/l5-c08-plotmatrices2.png

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Part 06-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-02-01-at-12.10.40-am.png

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Part 12-Module 01-Lesson 04_Probability/img/48738115.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48632800.gif

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/screen-shot-2017-09-03-at-2.28.22-pm.png

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/img/screen-shot-2018-06-13-at-6.32.38-pm.png

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Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-03-21-at-2.40.42-pm.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/multilayer-diagram-weights.png

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Part 03-Module 01-Lesson 04_Keras/img/data.png

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/data.png

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assets/js/bootstrap.min.js

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Part 02-Module 01-Lesson 09_Training and Tuning/img/circle-data.png

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Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-01-06-at-8.13.20-pm.png

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Part 02-Module 01-Lesson 06_Support Vector Machines/img/screen-shot-2018-01-06-at-8.13.20-pm.png

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/img/screen-shot-2018-02-14-at-10.47.52-am.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/input-times-weights.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/network-with-labeled-nodes.png

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Part 12-Module 01-Lesson 04_Probability/img/48741099.gif

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Part 12-Module 01-Lesson 04_Probability/img/48667981.gif

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Part 07-Module 01-Lesson 01_Basic SQL/img/screen-shot-2017-08-04-at-6.41.07-pm.png

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-24-at-2.17.54-pm.png

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Part 12-Module 01-Lesson 04_Probability/img/48698595.gif

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/img/screen-shot-2018-03-10-at-12.47.35-am.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/slides-choose-slide-type.png

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Part 12-Module 01-Lesson 03_Admissions Case Study/img/48729170.gif

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-branch-current.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/screen-shot-2018-05-26-at-7.04.15-pm.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/derivative-example.png

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Part 12-Module 01-Lesson 04_Probability/img/48699581.gif

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48720246.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48741058.gif

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-timeit2.png

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-09-21-at-12.02.03-pm.png

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Part 12-Module 01-Lesson 04_Probability/img/48742066.gif

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Part 12-Module 01-Lesson 04_Probability/img/48684742.gif

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Part 12-Module 01-Lesson 04_Probability/img/48687795.gif

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-05-25-at-11.27.26-am.png

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48746015.gif

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Part 12-Module 01-Lesson 04_Probability/img/48688828.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48635652.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48204962.gif

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-11-at-3.21.34-pm.png

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Part 12-Module 01-Lesson 04_Probability/img/48693692.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48750006.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48292975.gif

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Part 02-Module 01-Lesson 07_Ensemble Methods/img/anscombes-quartet-3.svg

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48716288.gif

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/network-with-labeled-weights.png

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48692666.gif

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assets/css/fonts/KaTeX_Main-Bold.ttf

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48230509.gif

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/screen-shot-2017-09-03-at-3.13.54-pm.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48632846.gif

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Part 08-Module 01-Lesson 06_Explanatory Visualizations/img/l6-c08-slidedeck1.png

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48738100.gif

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Part 12-Module 01-Lesson 04_Probability/img/48698583.gif

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/full-outer-join.png

61 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/slides-cell-toolbar-menu.png

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/full-outer-join-if-null.png

62 kB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-shutdown.png

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48716247.gif

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/img/points.png

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Part 20-Module 01-Lesson 01_Neural Networks/img/points.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/points.png

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Part 12-Module 01-Lesson 04_Probability/img/48695597.gif

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Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/convolution-schematic.gif

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Part 11-Module 01-Lesson 01_Introduction/img/cp1a9390.jpg

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48739104.gif

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-status-new-project.gif

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/img/ud456-l2-02-clone-linked-to-fork.png

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-29-at-11.49.47-am.png

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/left-join.png

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Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-01-06-at-9.30.27-pm.png

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/right-join.png

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Part 02-Module 01-Lesson 05_Naive Bayes/img/spam.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-nav-bar-new-repo-link.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-10-at-8.10.13-pm.png

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Part 12-Module 01-Lesson 03_Admissions Case Study/img/48680638.gif

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48734186.gif

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assets/css/fonts/KaTeX_Main-Regular.ttf

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Part 02-Module 01-Lesson 02_Linear Regression/img/just-a-2d-reg.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-pdb.png

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48725208.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48480558.gif

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/img/screen-shot-2017-06-26-at-2.11.18-pm.png

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Part 12-Module 01-Lesson 04_Probability/img/48667979.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48692663.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48683704.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48609553.gif

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-status-blog-project.gif

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-create-env.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48629196.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48697566.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48632799.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48296523.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48687733.gif

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/gradient-descent.png

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Part 14-Module 01-Lesson 02_Communicating to Stakeholders/img/screen-shot-2018-03-09-at-4.07.07-pm.png

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48746014.gif

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48652467.gif

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/nbconvert-example.png

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48678758.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48709280.gif

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Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-post.png

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Part 12-Module 01-Lesson 03_Admissions Case Study/img/48692636.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48241000.gif

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/img/disaster-response-project1.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48686674.gif

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-init.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48737119.gif

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Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/img/screen-shot-2017-06-26-at-3.47.37-pm.png

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Part 12-Module 01-Lesson 03_Admissions Case Study/img/48678737.gif

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Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2017-11-07-at-2.16.14-pm.png

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/img/gmm-2d-quiz.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/matrix-mult-3.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48230510.gif

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Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/img/screen-shot-2018-09-14-at-2.25.01-pm.png

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Part 07-Module 01-Lesson 01_Basic SQL/img/screen-shot-2017-08-02-at-11.14.25-am.png

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/likertscale.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-download.png

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Part 02-Module 01-Lesson 06_Support Vector Machines/img/polynomial-kernel-2-quiz.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48632848.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48658976.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48311831.gif

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Part 07-Module 01-Lesson 01_Basic SQL/img/screen-shot-2017-10-19-at-5.33.45-pm.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/erd.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48240998.gif

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/img/gmm-quiz.png

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Part 07-Module 01-Lesson 01_Basic SQL/img/screen-shot-2017-08-02-at-10.48.24-pm.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48704300.gif

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-14-at-1.12.55-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-install.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48011955.gif

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48750011.gif

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-base-directory.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48198839.gif

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48641639.gif

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-jupyter.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48739228.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/screen-shot-2017-08-02-at-4.57.01-pm.png

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Part 11-Module 01-Lesson 03_Linear Combination/img/screen-shot-2018-01-29-at-11.51.35-am.png

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Part 12-Module 01-Lesson 04_Probability/img/48667978.gif

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48726280.gif

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/img/inner-join.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48716290.gif

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assets/js/jquery-3.3.1.min.js

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-new.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48480561.gif

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48271966.gif

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/img/disaster-response-project2.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48743074.gif

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/img/resid2.jpg

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48310768.gif

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Part 03-Module 01-Lesson 03_Training Neural Networks/img/regularization-quiz.png

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/regularization-quiz.png

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-fetch-upstream-changes.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/48240997.gif

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-14-at-3.41.58-pm.png

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Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/media/New-Starbucks-Logo-1200x969.jpg

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Part 12-Module 01-Lesson 03_Admissions Case Study/img/48698525.gif

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-git-remotes-origin.png

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Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-2.28.03-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-matplotlib.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48734324.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/img/48684686.gif

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Part 02-Module 01-Lesson 04_Decision Trees/img/student-data.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/example-data.png

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Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-23-at-11.30.13-am.png

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Part 10-Module 01-Lesson 01_What is Version Control/img/ud123-l1-terminal-config-windows.png

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/img/perceptronquiz.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/perceptronquiz.png

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Part 20-Module 01-Lesson 01_Neural Networks/img/perceptronquiz.png

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/summary.png

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Part 03-Module 01-Lesson 04_Keras/img/summary.png

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-menu.png

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Part 20-Module 01-Lesson 01_Neural Networks/img/xor-quiz.png

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/img/xor-quiz.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/xor-quiz.png

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/media/unnamed-project-desc-0.gif

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Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-hard.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-json.png

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Part 06-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-01-30-at-4.39.42-pm.png

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Part 02-Module 01-Lesson 09_Training and Tuning/img/complexity.png

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Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-soft.png

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Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/img/screen-shot-2018-08-27-at-3.51.23-pm.png

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Part 12-Module 01-Lesson 06_Conditional Probability/img/48271967.gif

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Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/img/external-indices-quiz.png

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Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-04-02-at-4.25.41-pm.png

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-11-at-12.54.48-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/new-notebook.png

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/img/screen-shot-2018-02-14-at-3.59.39-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/notebook-server.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-14-at-4.04.44-pm.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-14-at-4.10.54-pm.png

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/apple.jpg

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/accuracy-quiz.png

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Part 20-Module 01-Lesson 02_Deep Neural Networks/img/nn.png

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-new-git-project.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/screen-shot-2017-08-02-at-11.40.37-am.png

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Part 02-Module 01-Lesson 09_Training and Tuning/img/learning-curves.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/conda-tab.png

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Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-log-p.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-new-git-project.png

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Part 13-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.38.47-pm.png

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Part 01-Module 02-Lesson 02_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.38.47-pm.png

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Part 02-Module 01-Lesson 04_Decision Trees/img/screen-shot-2018-01-06-at-9.41.01-pm.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-commit-terminal-hangs.png

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Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-24-at-2.27.07-pm.png

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-base-directory-git-repo.png

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Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.57.42-pm.png

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Part 07-Module 01-Lesson 02_SQL Joins/img/screen-shot-2017-08-10-at-8.23.48-pm.png

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Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/img/screen-shot-2018-02-14-at-6.07.26-pm.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/admissions-data.png

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Part 12-Module 01-Lesson 07_Bayes Rule/img/screen-shot-2017-08-02-at-11.49.10-am.png

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/natgeo-scatter.jpg

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-log-pre-tag.png

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Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-mixed.png

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Part 11-Module 01-Lesson 01_Introduction/img/screen-shot-2018-01-19-at-1.58.00-pm.png

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assets/js/plyr.polyfilled.min.js

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/img/screen-shot-2018-07-19-at-4.06.55-pm.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-project-commits.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-branch-asterisk.png

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Part 02-Module 01-Lesson 05_Naive Bayes/img/spamham.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/minibatch.png

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-add-upstream-remote.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-tag.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-git-remote-no-remote.png

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Part 02-Module 01-Lesson 04_Decision Trees/img/recommending-apps.png

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Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/img/screen-shot-2018-02-21-at-8.05.18-pm.png

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Part 17-Module 04-Lesson 01_Recommendation Engines/img/screen-shot-2018-09-17-at-3.40.30-pm.png

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-notebook.png

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-git-log-of-upstream-changes.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-log-branches.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-branch.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-my-travel-plans-project.png

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-gpu.png

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Part 03-Module 01-Lesson 06_Image Classifier Project/img/workspaces-submit.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-git-remote-shortname.png

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-git-clone.gif

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-04-commit-count-local.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/email.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-branch-sidebar.png

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Part 06-Module 01-Lesson 02_Data Types and Operators/img/screen-shot-2018-05-11-at-11.03.34-am.png

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-11-at-12.52.03-pm.png

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/img/ud456-l3-03-rename-repos.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-checkout-sidebar.png

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/challenger2.gif

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Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/img/ud456-l2-02-git-fork-error.png

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Part 10-Module 01-Lesson 06_Undoing Changes/img/ud123-l6-git-revert-prep.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/server-shutdown.png

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-05-25-at-11.27.36-am.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/img/magic-timeit.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-changes-add-color.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/command+palette.mp4

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/img/screen-shot-2018-08-13-at-6.26.18-pm.png

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Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2018-01-24-at-12.03.45-am.png

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Part 04-Module 01-Lesson 05_Random Projection and ICA/img/eeg-ica.png

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-07-at-6.02.41-pm.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/quiz.jpg

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Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-status-output.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-merge-sidebar.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-diff.png

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/img/mat-headshot.png

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Part 03-Module 01-Lesson 02_Implementing Gradient Descent/img/mat-headshot.png

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-git-tag-delete.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-project-on-github-focus.png

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Part 10-Module 01-Lesson 07_Working With Remotes/img/ud456-l1-project-on-github.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/medical.png

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Part 02-Module 01-Lesson 07_Ensemble Methods/img/screen-shot-2018-01-03-at-2.23.38-pm.png

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/img/confusion.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-gitignore.png

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Part 08-Module 01-Lesson 02_Design of Visualizations/img/pasted-image-0.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-status-all-files.png

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Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-ignore-word-doc.png

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Part 02-Module 01-Lesson 02_Linear Regression/img/batch-stochastic.png

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-02-23-at-5.11.40-pm.png

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Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/img/screen-shot-2018-07-19-at-4.05.25-pm.png

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/conda_install.mp4

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Part 11-Module 01-Lesson 02_Vectors/img/screen-shot-2018-01-24-at-3.13.49-pm.png

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-.git-directory.png

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Part 10-Module 01-Lesson 03_Review a Repo's History/img/ud123-l3-git-log-stat.gif

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Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/img/mike-josh-bios-portraits.png

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-24-at-2.18.30-pm.png

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/img/xor.png

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Part 20-Module 01-Lesson 01_Neural Networks/img/xor.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/img/xor.png

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Part 02-Module 01-Lesson 06_Support Vector Machines/img/margin-geometry-images.002.jpeg

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Part 01-Module 03-Lesson 01_Setting Up Your Computer/media/notebook+interface.mp4

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-11-at-12.52.21-pm.png

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Part 12-Module 01-Lesson 07_Bayes Rule/27. Robot Sensing 7-clFL503NPyY.mp4

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Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-tPSj6_m-0_M.mp4

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/screen-shot-2018-08-07-at-4.35.30-pm.png

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Part 13-Module 01-Lesson 02_Get Help from Peers and Mentors/img/screen-shot-2018-11-07-at-9.55.40-pm.png

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-PfEYA6z-19w.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-rjpcSymYulE.mp4

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Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-8fM13xqU2a8.mp4

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Part 02-Module 01-Lesson 06_Support Vector Machines/img/screen-shot-2018-06-02-at-5.52.44-pm.png

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Part 10-Module 01-Lesson 07_Working With Remotes/06. Pull Vs Fetch-kxXdk2HcOBo.mp4

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Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-dBax3E1AC2s.mp4

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Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-fSKL742j-zk.mp4

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Part 12-Module 01-Lesson 06_Conditional Probability/08. Medical Example 7-cw_zgQbAWNU.mp4

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Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-Y7tnbth-gag.mp4

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Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7.1 - Downloading Solution-1oEJUA-b0kE.mp4

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Part 03-Module 01-Lesson 03_Training Neural Networks/09. Local Minima-gF_sW_nY-xw.mp4

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Part 20-Module 01-Lesson 02_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-1PHs2w_NNTg.mp4

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 2-so5zydnbYEg.mp4

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Part 12-Module 01-Lesson 03_Admissions Case Study/05. Admissions 4-GD6cQhkoqS4.mp4

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Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2017-11-07-at-2.18.27-pm.png

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Part 07-Module 01-Lesson 02_SQL Joins/02. Why Not Store Everything in One Table-rvY4A6FpS40.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-GzRNoodJZxk.mp4

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Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Projects-1-E_ZYovKeI.mp4

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Part 12-Module 01-Lesson 12_Hypothesis Testing/img/screen-shot-2017-11-07-at-2.17.08-pm.png

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Part 12-Module 01-Lesson 16_Logistic Regression/19. Classifying Chavez Correctly 2-HWW9BNHnPo0.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/11. Minimum Value-LconwqN7hJs.mp4

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Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Git Pull In Theory-MjNU2LTDVAA.mp4

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Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 17 Git The Big Picture 2-rFtUkk-sCqw.mp4

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Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-TWGqylKdGWs.mp4

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Part 12-Module 01-Lesson 15_Multiple Linear Regression/img/screen-shot-2017-12-07-at-3.48.20-pm.png

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Part 03-Module 01-Lesson 03_Training Neural Networks/14. Learning Rate-TwJ8aSZoh2U.mp4

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/collage2.png

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Part 14-Module 01-Lesson 01_The Data Science Process/img/screen-shot-2018-02-24-at-2.16.00-pm.png

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. XOR Perceptron-TF83GfjYLdw.mp4

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Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/17. Measures of Center - The Mode-NE81NZgECqg.mp4

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/img/new-pymk-925x1024.png

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Part 10-Module 01-Lesson 02_Create A Git Repo/img/ud123-l2-course-git-blog-project-in-browser.png

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Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-HB8b7sZQFGs.mp4

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Part 02-Module 01-Lesson 02_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/img/48713571.gif

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-z2xsu2Kehyo.mp4

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Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-cdFrLeXIkZU.mp4

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Part 12-Module 01-Lesson 03_Admissions Case Study/03. Admissions 2-pJrwiukN3Ls.mp4

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Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/05. Outro-dVrYQ7o8a-k.mp4

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Part 07-Module 01-Lesson 02_SQL Joins/10. ALIAS-viWHJaxWTvw.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-5zkupL6EWh8.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-yTr8zCHdo5M.mp4

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Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/img/screen-shot-2018-05-29-at-4.19.03-pm.png

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Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-QWWq77k-K_0.mp4

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Part 07-Module 01-Lesson 03_SQL Aggregations/16. GROUP BY Part II-0HQ-TshNNQA.mp4

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Part 07-Module 01-Lesson 01_Basic SQL/03. Entity Relationship Diagrams-YY2TAJLEINA.mp4

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Part 09-Module 01-Lesson 01_Shell Workshop/10. Ud206 013 Shell P8 - Viewing Files-hPPVMKqbQV0.mp4

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Part 03-Module 01-Lesson 03_Training Neural Networks/11. Vanishing Gradient-W_JJm_5syFw.mp4

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Part 20-Module 01-Lesson 02_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.mp4

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Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. DataVis L3 11 V1-C8DGwJa_adA.mp4

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Part 16-Module 01-Lesson 02_ETL Pipelines/41. 52 Putting It All Together V1 1 V1-D2Th0KdPI-Y.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4

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Part 20-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4

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Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/11. Intro To Collab Filtering-wGq7dUgJpsc.mp4

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Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-P4uljJ_OP6I.mp4

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 1-APRpwqFpGwI.mp4

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Part 02-Module 01-Lesson 08_Model Evaluation Metrics/20. Outro SC V1-YD1grQje9fw.mp4

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Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/17. Performance Tuning 3-hIAE8W6x5O8.mp4

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Part 08-Module 01-Lesson 07_Visualization Case Study/01. L7 011 Intro V1-Virihwp36do.mp4

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Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/20. Percentiles-Qro8uvysnys.mp4

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Part 02-Module 01-Lesson 05_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4

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Part 04-Module 01-Lesson 01_Clustering/21. Outro-AeDSl4KSVIE.mp4

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Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-SYnYIjLpbjE.mp4

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Part 16-Module 01-Lesson 02_ETL Pipelines/11. Transform Walk Through-i9_0kHCCCCE.mp4

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Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 71 Merging-gQiWicrreJg.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-S87Z5DgPJeo.mp4

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Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Chain Rule-YAhIBOnbt54.mp4

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Part 02-Module 01-Lesson 02_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4

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Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3--6l4_oprDOk.mp4

1.5 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-DeWp0hnRq4g.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-VEfOdACY9rA.mp4

1.5 MB

Part 12-Module 01-Lesson 07_Bayes Rule/15. Disease Test 2-FQM7i07EqGo.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4

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Part 20-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4

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Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-0PFq8zoaNWU.mp4

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Part 12-Module 01-Lesson 04_Probability/13. One Head 1-T4A5uyqesjo.mp4

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Part 02-Module 01-Lesson 06_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4

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Part 07-Module 01-Lesson 03_SQL Aggregations/30. CASE Statements and Aggregations-asSXB6iD3z4.mp4

1.6 MB

Part 01-Module 02-Lesson 02_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.48.22-pm.png

1.6 MB

Part 13-Module 01-Lesson 03_Get Help with Your Account/img/screen-shot-2018-11-09-at-7.48.22-pm.png

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Part 12-Module 01-Lesson 03_Admissions Case Study/04. Admissions 3-iKTYAsZLbhc.mp4

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Part 12-Module 01-Lesson 07_Bayes Rule/26. Robot Sensing 6-hXyXlk0gYzk.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/04. 5 Flips 1 Head-4LVRNqpdxsw.mp4

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Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/06. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4

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Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 1-HPmMEkbT2uE.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-N-wpkttwcoA.mp4

1.6 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/03. Heads Tails 2-vLhdJtXx060.mp4

1.6 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.mp4

1.6 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. DataVis L5C05 V1-v19gCP4TvwE.mp4

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Part 12-Module 01-Lesson 07_Bayes Rule/16. Disease Test 3-a61GPGk-Qy4.mp4

1.6 MB

Part 12-Module 01-Lesson 07_Bayes Rule/37. Bayes Rule Conclusion-vlfDGCD8w0s.mp4

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Part 02-Module 01-Lesson 03_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4

1.6 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/04. Classification Example-46PywnGa_cQ.mp4

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Part 20-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4

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Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-w5qcGO8krMw.mp4

1.6 MB

Part 07-Module 01-Lesson 01_Basic SQL/30. Arithmetic Operators-fgcJdiNECxI.mp4

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Part 12-Module 01-Lesson 08_Python Probability Practice/01. Python Probability Introduction-tFMdvAN7WDY.mp4

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Part 20-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4

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Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Theory-twLr9ndsf90.mp4

1.6 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/17. 05 Docstrings V1-_gapemxsRJY.mp4

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Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/img/screen-shot-2018-09-21-at-11.36.43-am.png

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Part 12-Module 01-Lesson 06_Conditional Probability/07. Medical Example 6--lC9xztr4zA.mp4

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Part 04-Module 01-Lesson 04_PCA/04. Latent Features-kYLcVgpEwGs.mp4

1.7 MB

Part 12-Module 01-Lesson 04_Probability/06. Loaded Coin 3-HohMRlmHoMQ.mp4

1.7 MB

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-pL8Bf6tck_A.mp4

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Part 04-Module 01-Lesson 01_Clustering/05. KMeans-B9jdQFpPEk0.mp4

1.7 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-tiv8VKPL7jg.mp4

1.7 MB

Part 07-Module 01-Lesson 01_Basic SQL/46. OR Statement-DRmkKVhe6-s.mp4

1.7 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4

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Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4

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Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4

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Part 20-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4

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Part 20-Module 01-Lesson 02_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4

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Part 03-Module 01-Lesson 03_Training Neural Networks/16. Error Functions Around the World-34AAcTECu2A.mp4

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Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-CQHRYIU6v9Q.mp4

1.7 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/img/screen-shot-2018-05-29-at-4.06.53-pm.png

1.7 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/07. Categorical Ordinal Nominal Data-k5bLaPGY2Vw.mp4

1.7 MB

Part 12-Module 01-Lesson 07_Bayes Rule/25. Robot Sensing 5-PGG9agooCvw.mp4

1.7 MB

Part 09-Module 01-Lesson 01_Shell Workshop/11. Ud206 014 Shell P9 - Removing Things-it19PvJarbk.mp4

1.7 MB

Part 02-Module 01-Lesson 09_Training and Tuning/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4

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Part 07-Module 01-Lesson 01_Basic SQL/37. IN Operator-_JPO7wwX3uA.mp4

1.8 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-6LO6I5M18PQ.mp4

1.8 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/17. Funk SVD Review-nc3GMIrISHE.mp4

1.8 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/20. L2 02 Outro REPLACEMENT-W-6Se0G_FVE.mp4

1.8 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/06. Gender Bias-JWl8lPGhlbY.mp4

1.8 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/09. Maximum Value-z_eElEkVOPY.mp4

1.8 MB

Part 12-Module 01-Lesson 16_Logistic Regression/26. Practicing TP, FP, FN with Rumsfeld-C3Fqiu6HJog.mp4

1.8 MB

Part 02-Module 01-Lesson 05_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4

1.8 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION 3-oVGmi4zBOT8.mp4

1.8 MB

Part 09-Module 01-Lesson 01_Shell Workshop/05. Ud206 006 Shell P3 - Navigating Directories-i9Xp94DmdB8.mp4

1.8 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/01. 26 Spread Part 1-zb76Z_viYLY.mp4

1.8 MB

Part 02-Module 01-Lesson 02_Linear Regression/11. Mean Squared Error-MRyxmZDngI4.mp4

1.8 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. Data Vis L4 C07 V1-f6v3L3IDo24.mp4

1.8 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Subqueries Using WITH-IszTmDKyKHI.mp4

1.8 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.mp4

1.9 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/11. Binomial 2-Uy7b3aMPnEY.mp4

1.9 MB

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4

1.9 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4

1.9 MB

Part 12-Module 01-Lesson 16_Logistic Regression/18. Classifying Chavez Correctly 1-Jbqf8OBORDg.mp4

1.9 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/31. Descriptive Statistics Summary-Fe7Gta2SfLA.mp4

1.9 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Multiclass Classification-uNTtvxwfox0.mp4

1.9 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.mp4

1.9 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-lhhUjxnbad8.mp4

1.9 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/22. L2 18 Version Control Git Commit Messages V1 V2-w1iHWpwOkMg.mp4

1.9 MB

Part 07-Module 01-Lesson 01_Basic SQL/40. NOT Operator-dSQF87oW8a0.mp4

1.9 MB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Using With-OQ-Y0mMjm00.mp4

1.9 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/05. Types of Machine Learning - Unsupervised Reinforcement-yg4A99NMzAQ.mp4

1.9 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4

1.9 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4

1.9 MB

Part 20-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4

1.9 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-x1yamZeOMPY.mp4

1.9 MB

Part 07-Module 01-Lesson 01_Basic SQL/46. OR Operator-3vLGEuXAAvA.mp4

1.9 MB

Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4

1.9 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4

1.9 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 1-SNFHYbJvlZU.mp4

1.9 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/10. Stop Word Removal-WAU_Ij0GJbw.mp4

2.0 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/14. Conclusion-XiR_37bYA84.mp4

2.0 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4

2.0 MB

Part 20-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4

2.0 MB

Part 04-Module 01-Lesson 01_Clustering/17. Feature Scaling Example--Axyt0bPCT0.mp4

2.0 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. 13 Inheritance Example V1-uWT-HIHBjv0.mp4

2.0 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/03. Testing-EeBZpb-PSac.mp4

2.0 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/07. Quadratics 4-yimIE9fCvi8.mp4

2.0 MB

Part 10-Module 01-Lesson 04_Add Commits To A Repo/img/ud123-l4-git-add-to-staging-recap.gif

2.0 MB

Part 09-Module 01-Lesson 01_Shell Workshop/13. Ud206 017 Shell P11 - Variables-Dx3WlMZk8iA.mp4

2.0 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/01. Introduction-LcX-s-ujp7U.mp4

2.0 MB

Part 07-Module 01-Lesson 01_Basic SQL/06. Why Businesses Choose Databases-j4ey7--h9r8.mp4

2.0 MB

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-aBBmlnd7okQ.mp4

2.0 MB

Part 12-Module 01-Lesson 16_Logistic Regression/13. Filling in a Confusion Matrix-Lb_v4vj3TNs.mp4

2.0 MB

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-7k5oAaZamCA.mp4

2.0 MB

Part 15-Module 01-Lesson 06_Web Development/19. The World Wide Web-Rxn-zCyg_iA.mp4

2.0 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/05. COUNT NULLs-ngxgqfFFFLQ.mp4

2.0 MB

Part 02-Module 01-Lesson 09_Training and Tuning/13. MLND Outro-sFvMBncQjr8.mp4

2.1 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4

2.1 MB

Part 12-Module 01-Lesson 08_Python Probability Practice/08. Python Probability Conclusion-4JYar5GykXk.mp4

2.1 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.mp4

2.1 MB

Part 09-Module 01-Lesson 01_Shell Workshop/07. Ud206 008 Shell P5 - Parameters-UX9mzq11Mmg.mp4

2.1 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-NRPcnpmFCg8.mp4

2.1 MB

Part 20-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4

2.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4

2.1 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.mp4

2.1 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/05. Data Types-gT6EYlsLZkE.mp4

2.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/21. 29 Missing Data Delete V1 V2-L0MoPGyiiYo.mp4

2.1 MB

Part 12-Module 01-Lesson 07_Bayes Rule/07. Total Probability-fAaE5K9OZJc.mp4

2.1 MB

Part 20-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4

2.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4

2.1 MB

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-rxfHfjy9Mm4.mp4

2.1 MB

Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-FnNveASivMA.mp4

2.1 MB

Part 09-Module 01-Lesson 01_Shell Workshop/04. Ud206 004 Shell P2 - Your First Command-ggf5WhOYy1U.mp4

2.1 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/01. Admissions Case Study Introduction-FGbxq1hQgtk.mp4

2.1 MB

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Rf6WfB_1EJQ.mp4

2.1 MB

Part 10-Module 01-Lesson 06_Undoing Changes/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L6 17 Soft Vs Medium Vs Hard Walkthrough-UN7ki2G2yKc.mp4

2.1 MB

Part 04-Module 01-Lesson 01_Clustering/07. 07 Changing K 1 V3-Bd3M-xUlqEI.mp4

2.1 MB

Part 12-Module 01-Lesson 14_Regression/15. Fitting A Regression Line In Python-0CiMDbEUeS4.mp4

2.1 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/17. Data Cleaning Conclusion-KkHqnvD9BWY.mp4

2.1 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/12. Analyzing Multiple Metrics Pt 2-x7foG7murvU.mp4

2.1 MB

Part 07-Module 01-Lesson 01_Basic SQL/27. WHERE with Non-Numeric Data-_pLx7MHOyjo.mp4

2.1 MB

Part 07-Module 01-Lesson 02_SQL Joins/03. Your First JOIN-HkX9fkNRbU8.mp4

2.1 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/03. Data Types and NULLs-RgTcYwKqtYI.mp4

2.1 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. L5 061 Other Adaptations Of Bivariate Plots V3-qanSZttNzFM.mp4

2.1 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. Data Vis L4 C12 V2-aJncRqqJUYI.mp4

2.1 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/01. Case Study Introduction-J5uvdPxHIfs.mp4

2.1 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/01. L2 011 Intro HD V2-TlpGWQBLG6E.mp4

2.1 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4

2.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/30. Non-Linear Data-F7ZiE8PQiSc.mp4

2.1 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/15. Momentum-r-rYz_PEWC8.mp4

2.1 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4

2.1 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/18. Conclusion-qmGjRpMVBz8.mp4

2.1 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/12. 07 Recall SC V1-0n5wUZiefkQ.mp4

2.1 MB

Part 12-Module 01-Lesson 04_Probability/12. Two Flips 5-G28YyiGFGWA.mp4

2.1 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/08. When Accuracy Wont Work-r0-O-gIDXZ0.mp4

2.2 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/12. Part-of-Speech Tagging-WFEu8bXI5OA.mp4

2.2 MB

Part 12-Module 01-Lesson 14_Regression/01. Regression Introduction-PKqSS0TzXeA.mp4

2.2 MB

Part 04-Module 01-Lesson 01_Clustering/03. Two Types of Unsupervised Learning-aHK_rpaS_ts.mp4

2.2 MB

Part 02-Module 01-Lesson 04_Decision Trees/13. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4

2.2 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/22. Having-D4gmN0vnk58.mp4

2.2 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/09. Aggregation 3-YkaVgZ-yFrM.mp4

2.2 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. L5 091 Feature Engineering V2-jpMOSFMMga4.mp4

2.2 MB

Part 12-Module 01-Lesson 16_Logistic Regression/28. Equation for Recall-j2SP83afRS0.mp4

2.2 MB

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-V_Gqm42WodI.mp4

2.2 MB

Part 07-Module 01-Lesson 01_Basic SQL/34. LIKE Operator-O5z6eWkNip4.mp4

2.2 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/05. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4

2.2 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/04. Intro To MovieTweetings-cuXvLIkq_W8.mp4

2.2 MB

Part 09-Module 01-Lesson 01_Shell Workshop/06. Ud206 007 Shell P4 - Current Working Directory-X7dsy3oMHp0.mp4

2.2 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/03. What Is Coming Up-oDJsnQcCPr4.mp4

2.2 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4

2.2 MB

Part 03-Module 01-Lesson 04_Keras/06. Keras Lab-a50un22BsLI.mp4

2.2 MB

Part 14-Module 01-Lesson 01_The Data Science Process/20. Predicting Salary-g1ZAn02ETK4.mp4

2.2 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/05. Confidence Interval for a Difference In Means-8hrWGzjyhck.mp4

2.2 MB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.mp4

2.2 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/09. Data Types Summary-T-KrQoAJUpI.mp4

2.2 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.mp4

2.2 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/09. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4

2.2 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/10. Answer False Negatives And Positives-KOytJL1lvgg.mp4

2.2 MB

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-9SbUxcyDTaQ.mp4

2.2 MB

Part 14-Module 01-Lesson 01_The Data Science Process/01. Introduction-VpxATYHhKM8.mp4

2.2 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/23. Window Functions Conclusion-2ZdocDMw7D8.mp4

2.2 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/11. 06 Precision SC V1-q2wVorBfefU.mp4

2.2 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/14. Analyzing Multiple Metrics-DtZghKNa7Ak.mp4

2.2 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/08. Quick Fixes #2-It6AEuSDQw0.mp4

2.3 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/14. COALESCE-86vgu-ECBCQ.mp4

2.3 MB

Part 12-Module 01-Lesson 04_Probability/14. One Head 2-JHx3ucNS9f4.mp4

2.3 MB

Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4

2.3 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4

2.3 MB

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-CStZqZRe6Mk.mp4

2.3 MB

Part 12-Module 01-Lesson 06_Conditional Probability/05. Medical Example 4-udduksMWMB4.mp4

2.3 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/13. Aliases for Multiple Window Functions-RWe03bULYnM.mp4

2.3 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/12. Other Activation Functions-kA-1vUt6cvQ.mp4

2.3 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4

2.3 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/05. 05 Extraction Idea 1 V1 V2-4dKG_08zMm4.mp4

2.3 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/09. Experiment II-fq4eO7CybA4.mp4

2.3 MB

Part 17-Module 02-Lesson 03_AB Testing Case Study/14. Conclusion-2G6x3oQnjy4.mp4

2.3 MB

Part 04-Module 01-Lesson 04_PCA/06. How to Reduce Features-ydhrelgjriI.mp4

2.3 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/04. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.mp4

2.3 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/07. Scikit Learn-kxvmG8ZsOVg.mp4

2.3 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/06. Subqueries Part II-jko-RrZd0R8.mp4

2.3 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/06. Accuracy-s6SfhPTNOHA.mp4

2.3 MB

Part 12-Module 01-Lesson 07_Bayes Rule/22. Robot Sensing 2-t22oDruXhuo.mp4

2.4 MB

Part 12-Module 01-Lesson 06_Conditional Probability/06. Medical Example 5-ys9w-NNKCcU.mp4

2.4 MB

Part 07-Module 01-Lesson 01_Basic SQL/43. AND BETWEEN Operators-nBuDPneWcKY.mp4

2.4 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. L3 041 Absolute V Relative Frequency V5-FpnZ7dH4FqU.mp4

2.4 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/01. Introduction-2Y279421n3A.mp4

2.4 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4

2.4 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-RBfFHxEjsIU.mp4

2.4 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Theory-H5JqcdIB5y0.mp4

2.4 MB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/08. L1 08.1 Lesson Summary HD (1)--c9IeqHkAZ0.mp4

2.4 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-U3FUxkm1MxI.mp4

2.4 MB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/02. Gradient Descent-29PmNG7fuuM.mp4

2.5 MB

Part 09-Module 01-Lesson 01_Shell Workshop/16. Ud206 021 Shell P14 Aliases-kINmpgXxayM.mp4

2.5 MB

Part 07-Module 01-Lesson 01_Basic SQL/18. ORDER BY Statement-wqj2As31LqI.mp4

2.5 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/16. Using A Confidence Interval to Make A Decision-MghT95b6LbQ.mp4

2.5 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/06. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4

2.5 MB

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-VLLG0rYC7To.mp4

2.5 MB

Part 12-Module 01-Lesson 16_Logistic Regression/17. How Many Schroeder Predictions-n7gp8USw0Jw.mp4

2.5 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/11. Dangers Of Statistics-UYZXqP562qg.mp4

2.5 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/14. Binomial 5-8jcCGD986jk.mp4

2.5 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/03. Better Formula-vMAl1m8ZtoI.mp4

2.5 MB

Part 11-Module 01-Lesson 02_Vectors/02. Vectors 2-R7WiQYixvRQ.mp4

2.5 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/20. 02 TF-IDF-LYYWIrWbBq4.mp4

2.5 MB

Part 04-Module 01-Lesson 04_PCA/21. Outro-CuIqzL8HjI8.mp4

2.5 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/38. Conclusion-LLEZadlXM8A.mp4

2.5 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-YSMWpFM92S0.mp4

2.5 MB

Part 09-Module 01-Lesson 01_Shell Workshop/08. Ud206 009 Shell P6 - Organizing Your Files-NZsYyzzpJXA.mp4

2.5 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/08. Running A Python Script-vMKemwCderg.mp4

2.6 MB

Part 06-Module 01-Lesson 05_Scripting/05. Running A Python Script-vMKemwCderg.mp4

2.6 MB

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-41HCYR-NW-w.mp4

2.6 MB

Part 02-Module 01-Lesson 02_Linear Regression/10. Mean Absolute Error-vLKiY0Ehors.mp4

2.6 MB

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-611qWzIxGmU.mp4

2.6 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/02. Histograms-4t10RgUv2Fc.mp4

2.6 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4

2.6 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/08. Self JOINs-tw_VzEGBOvI.mp4

2.6 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/11. UNION Motivation-0eRr2K8lo-I.mp4

2.6 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4

2.6 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4

2.6 MB

Part 20-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4

2.6 MB

Part 10-Module 01-Lesson 06_Undoing Changes/05. Undoing Changes--_PPVk2UZMU.mp4

2.6 MB

Part 10-Module 01-Lesson 07_Working With Remotes/03. L1 - New Repo Git Commands On GitHub-myuGLZLYpYY.mp4

2.6 MB

Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.mp4

2.6 MB

Part 17-Module 02-Lesson 03_AB Testing Case Study/01. Intro-28mN6RvGXDM.mp4

2.6 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/06. Quadratics 3-Ny2vcRZ6Aws.mp4

2.6 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/16. Subquery Conclusion-TUYvx2K9-5k.mp4

2.6 MB

Part 02-Module 01-Lesson 05_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4

2.6 MB

Part 12-Module 01-Lesson 04_Probability/04. Loaded Coin 1-T0EjWSjLGjQ.mp4

2.6 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/07. Knowledge Based Recommendations-C_vU1tjQHZI.mp4

2.6 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/10. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4

2.7 MB

Part 02-Module 01-Lesson 02_Linear Regression/19. Higher Dimensions--UvpQV1qmiE.mp4

2.7 MB

Part 14-Module 01-Lesson 01_The Data Science Process/38. Working With Categorical Variables-IoQOiuxsIZg.mp4

2.7 MB

Part 14-Module 01-Lesson 01_The Data Science Process/10. The Data Science Process Gathering And Wrangling-GvyfIiJUXWg.mp4

2.7 MB

Part 10-Module 01-Lesson 06_Undoing Changes/06. Course Outro-twn_cheqoK8.mp4

2.7 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-MZoYGBZTh-g.mp4

2.7 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/14. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4

2.7 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/09. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.7 MB

Part 20-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.7 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.7 MB

Part 12-Module 01-Lesson 06_Conditional Probability/02. Medical Example 1-mFfbts1lAEo.mp4

2.7 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/05. L5 051 Faceting In Two Directions V3-lz5dcoTcV2o.mp4

2.7 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/17. Two Useful Theorems - Central Limit Theorem-L79u8ywRmG8.mp4

2.7 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/01. 01 Intro-4C4PuJANIdE.mp4

2.7 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/10. Three Steps To Captivate Your Audience-BWS3oQYS-c4.mp4

2.7 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/01. Introduction-SvdlBB-ZjcQ.mp4

2.8 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-mvJUNYfHngY.mp4

2.8 MB

Part 10-Module 01-Lesson 01_What is Version Control/02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.mp4

2.8 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/13. Participating in open source projects-OxL-gMTizUA.mp4

2.8 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.mp4

2.8 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/01. Confidence Intervals Introduction-crleT4000ak.mp4

2.8 MB

Part 12-Module 01-Lesson 16_Logistic Regression/21. Powell Precision and Recall-q_zfkCwRg1w.mp4

2.8 MB

Part 08-Module 01-Lesson 07_Visualization Case Study/07. L7 0F1 Congrats V3-LF-obnL7CI0.mp4

2.8 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/04. Types Of Machine Learning - Supervised-Jn3xugBvs2U.mp4

2.8 MB

Part 12-Module 01-Lesson 14_Regression/02. Introduction to Machine Learning-pLcFPPI1L-0.mp4

2.8 MB

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-05upwXtARuo.mp4

2.8 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/04. What is Data-ldTDAjrVsA8.mp4

2.8 MB

Part 10-Module 01-Lesson 07_Working With Remotes/07. Lesson Wrap Up-6Koa4nAu-04.mp4

2.8 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/01. L2 2 01 Intro V1 V2-QO2GYq8q92E.mp4

2.8 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/02. Introduction to Subqueries-s8ZJMj4gscY.mp4

2.8 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4

2.8 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4

2.8 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/06. Up and Running On Medium-0QzbxjAcMq0.mp4

2.8 MB

Part 06-Module 01-Lesson 05_Scripting/29. Conclusion-rEMrswkLvh8.mp4

2.8 MB

Part 02-Module 01-Lesson 02_Linear Regression/21. Closed Form Solution-G3fRVgLa5gI.mp4

2.8 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/25. L2 21 Conclusion V1 V1-anPnokWZOZQ.mp4

2.8 MB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/06. Multilayer perceptrons-Rs9petvTBLk.mp4

2.8 MB

Part 12-Module 01-Lesson 07_Bayes Rule/10. Cancer Probabilities-CMQBKuYjPBM.mp4

2.8 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/01. Hypothesis Testing Introduction-Qi6F2rJAmrA.mp4

2.8 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/09. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4

2.9 MB

Part 03-Module 01-Lesson 06_Image Classifier Project/01. PROJECT INTRO MAIN V2---9IFCNBM6Y.mp4

2.9 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. INTRODUÇÃO AO PROJETO PRINCIPAL V2---9IFCNBM6Y.mp4

2.9 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/01. 01 Intro V1 V3-Zl_es7xtSqk.mp4

2.9 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.9 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.9 MB

Part 20-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.9 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/16. Comparing Row to Previous Row-Z_x5ZJyDZog.mp4

2.9 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/01. Instructors Introduction-lIvm8urf4GE.mp4

2.9 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. L3 081 Histograms V2-RLez9L0htGQ.mp4

2.9 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/06. 5 Number Summary to Variance-Ljhau0hrZ1g.mp4

2.9 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png

2.9 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/19. Conclusion-_ATzG6khLdk.mp4

2.9 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment-fH_xF5_SDCE.mp4

2.9 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4

2.9 MB

Part 12-Module 01-Lesson 07_Bayes Rule/33. Bayes' Rule and Robotics-meNSO42JF6I.mp4

2.9 MB

Part 15-Module 01-Lesson 06_Web Development/07. Div and Span-cbKA_dvthcY.mp4

2.9 MB

Part 12-Module 01-Lesson 07_Bayes Rule/04. Normalizing 1-5Tbd3_a5Vug.mp4

2.9 MB

Part 12-Module 01-Lesson 14_Regression/04. Introduction to Linear Regression-RD4zbBvXDnM.mp4

2.9 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.mp4

2.9 MB

Part 15-Module 01-Lesson 06_Web Development/26. Flask Pandas Plotly Part3-e8owK5zk-g8.mp4

3.0 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/13. Binomial 4-lPrKmvckG4E.mp4

3.0 MB

Part 04-Module 01-Lesson 04_PCA/07. Dimensionality Reduction-mANti9veGtc.mp4

3.0 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/03. Text Processing-pqheVyctkNQ.mp4

3.0 MB

Part 10-Module 01-Lesson 02_Create A Git Repo/05. Create A Repo - Outro-h7j4STDFCjs.mp4

3.0 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4

3.0 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/02. Training Optimization-UiGKhx9pUYc.mp4

3.0 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/07. Latent Factors-jZz7tFEF2Dc.mp4

3.0 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4

3.0 MB

Part 12-Module 01-Lesson 04_Probability/11. Two Flips 4-bNoS6LQEFrI.mp4

3.0 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/25. Word2Vec-7jjappzGRe0.mp4

3.0 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/27. Goals Of Recommendation Systems-WzelOlFeDmU.mp4

3.0 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/22. Virtual Environments-f7rzxUiHOJ0.mp4

3.0 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/18. L2 181 Lesson Summary HDmp4 V3-kKEeBDs4HuM.mp4

3.0 MB

Part 12-Module 01-Lesson 04_Probability/09. Two Flips 2-uhrL5fatt3E.mp4

3.0 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/03. L3 - Include Upstream Changes-VvoC6hN6FjU.mp4

3.0 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/05. Experiment I-JLKAdT2JESk.mp4

3.0 MB

Part 20-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4

3.0 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4

3.0 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. Data Vis L4 C06 V2-f8Kh4PByiEA.mp4

3.0 MB

Part 09-Module 01-Lesson 01_Shell Workshop/03. Ud206 003 Shell P1 - Opening A Terminal-4q6Vtym-nno.mp4

3.0 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/08. L2 2 11 Logging V2-9qKQdRoIMbU.mp4

3.0 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/12. Reflect on your commit messages-_0AHmKkfjTo.mp4

3.0 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/01. Lesson Introduction-rw3YaQ2CTNQ.mp4

3.0 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/26. Notation for the Mean-3EF15AoRxyM.mp4

3.0 MB

Part 12-Module 01-Lesson 14_Regression/08. Correlation Coefficients-rL5Bn8Fi-zE.mp4

3.0 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.mp4

3.0 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. DataVis L3 12 V2-fo0VIbQRBJk.mp4

3.0 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/20. Using Grid Search-iTL43Jk9_bQ.mp4

3.1 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/19. Congratulations!-_FPpbuuW-1o.mp4

3.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/39. 47 Load V1 V1-Us1hWDaabxo.mp4

3.1 MB

Part 12-Module 01-Lesson 16_Logistic Regression/23. True Positives in Eigenfaces-bgT8sWuV2lc.mp4

3.1 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/09. L6 10 V1 V6-LoYT4NMSPGk.mp4

3.1 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4

3.1 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/07. Types Of Errors - Part I-aw6GMxIvENc.mp4

3.1 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/15. Captivate Your Audience - End With A Call To Action-EajX2NbHJ6w.mp4

3.1 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png

3.1 MB

Part 15-Module 01-Lesson 06_Web Development/32. L4 Outro V2-8MyuJx5yu38.mp4

3.1 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/09. DataVis L5C09 V1-xlZ9AMV6VUE.mp4

3.1 MB

Part 07-Module 01-Lesson 02_SQL Joins/14. JOINs-CxuHtd1Daqk.mp4

3.1 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/12. 12 Pipelines And Feature Unions V1 V3-zduxy0g23L0.mp4

3.1 MB

Part 09-Module 01-Lesson 01_Shell Workshop/12. Ud206 016 Shell P10 - Searching And Pipes-AWpVScp9z4s.mp4

3.1 MB

Part 06-Module 01-Lesson 03_Control Flow/34. Congrats!-vDoqpwCHxs4.mp4

3.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Layers-pg99FkXYK0M.mp4

3.1 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4

3.1 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/16. Performance Tuning 2-arMtEhSoq7E.mp4

3.1 MB

Part 09-Module 01-Lesson 01_Shell Workshop/14. Ud206 018 P12 Startup Files (.bash_profile)--zF-XebfzBE.mp4

3.1 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/06. Normalization-eOV2UUY8vtM.mp4

3.1 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/19. Introduction to Percentiles-t7SX2ZEdxKA.mp4

3.1 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. Data Vis L4 C11 V1-3Ls6w8Cd8n4.mp4

3.2 MB

Part 06-Module 01-Lesson 05_Scripting/13. Handling Error Specifying Exceptions-EHW5I7shdJg.mp4

3.2 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/10. Aggregates in Window Functions-Dxew5w3VF7k.mp4

3.2 MB

Part 09-Module 01-Lesson 01_Shell Workshop/09. Ud206 011 Shell P7 - Downloading-h7FhU1f4TgE.mp4

3.2 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/17. Shape of Distributions-UnN99AAYf8k.mp4

3.2 MB

Part 07-Module 01-Lesson 01_Basic SQL/21. Order By Part II-XQCjREdOqwE.mp4

3.2 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. L5 081 Plot Matrices V3-2wY-euTIE5g.mp4

3.2 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4

3.2 MB

Part 20-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4

3.2 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/23. 24 Conclusion V1 V2-Jq6pj_uKDmY.mp4

3.2 MB

Part 12-Module 01-Lesson 14_Regression/21. Recap-DzMi27LI5l4.mp4

3.2 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/04. Practical Significance-eJ3idt3AJ7E.mp4

3.2 MB

Part 04-Module 01-Lesson 04_PCA/17. When to Use PCA-arSP83-CM6w.mp4

3.2 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/08. Tokenization-4Ieotbeh4u8.mp4

3.2 MB

Part 02-Module 01-Lesson 05_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4

3.2 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/01. Intro-j5RmK0UHOTY.mp4

3.2 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/01. Introduction to Advanced SQL-i0VaVPIKUks.mp4

3.2 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/24. There Must Be A Better Way-oBp8YX2AgJw.mp4

3.2 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/01. Intro-svCesgAQ46Q.mp4

3.2 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/20. 28 Missing Data Causes V1 V2-zlw8ESS6Q88.mp4

3.2 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/11. Introduction To Notation-ISkBSUVH49M.mp4

3.2 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. Data Vis L4 C04 V1-O6ElT4IFXc0.mp4

3.2 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/07. Running Totals And Count-rNJwmnzUTxg.mp4

3.3 MB

Part 02-Module 01-Lesson 02_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4

3.3 MB

Part 10-Module 01-Lesson 07_Working With Remotes/05. L1 - Adding A Commit On GitHub-UBYxcTg6VLU.mp4

3.3 MB

Part 04-Module 01-Lesson 01_Clustering/04. 04 KMeans Use Cases 1 1 V2-25paySwVdAA.mp4

3.3 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.mp4

3.3 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/15. Participating in open source projects 2-elZCLxVvJrY.mp4

3.3 MB

Part 20-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4

3.3 MB

Part 15-Module 01-Lesson 06_Web Development/03. L4 Components Of A Web App V4-2aJf5sO2ox4.mp4

3.3 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4

3.3 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Calculating The Gradient 1 -tVuZDbUrzzI.mp4

3.3 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/11. Metric - Average Reading Duration-w6Y9ZxHDEbw.mp4

3.3 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/06. PyTorch - Part 4-AEJV_RKZ7VU.mp4

3.3 MB

Part 12-Module 01-Lesson 14_Regression/10. What Defines A Line-lTqwhsSNP2c.mp4

3.3 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/06. L4 061 Violin Plots 2 V3-0hr61L-LZyM.mp4

3.3 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/04. Types Of Sampling-GF_eQqNoarI.mp4

3.3 MB

Part 12-Module 01-Lesson 07_Bayes Rule/28. Robot Sensing 8-lmuonrQp_lM.mp4

3.3 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/17. Regression-Metrics-906P4BPnl9A.mp4

3.4 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/01. L4 011 Intro V2-JzvJIWG8Rk4.mp4

3.4 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/03. GitHub profile important items-prvPVTjVkwQ.mp4

3.4 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/14. Correct Interpretations of Confidence Intervals-IhYv_SlN7e8.mp4

3.4 MB

Part 12-Module 01-Lesson 04_Probability/05. Loaded Coin 2-dGffszQYzqc.mp4

3.4 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-Qm4KTLfFMzo.mp4

3.4 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4

3.4 MB

Part 06-Module 01-Lesson 05_Scripting/11. Errors And Exceptions-DmthSiy2d0U.mp4

3.4 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/01. Natural Language Processing-UQBxJzoCp-I.mp4

3.4 MB

Part 09-Module 01-Lesson 01_Shell Workshop/17. Ud206 022 Shell Workshop Outro-68twTPXPrx0.mp4

3.4 MB

Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/01. Intro-VkqtlJuZ9rs.mp4

3.4 MB

Part 12-Module 01-Lesson 08_Python Probability Practice/04. Simulating Many Coin Flips-AqpWQIj2V5Y.mp4

3.4 MB

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-yUIz7SgUwJg.mp4

3.4 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-O-4qRh74rkI.mp4

3.4 MB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/07. Backpropagation-MZL97-2joxQ.mp4

3.4 MB

Part 02-Module 01-Lesson 09_Training and Tuning/08. Grid Search SC V1-zDw-ZGiHW5I.mp4

3.4 MB

Part 12-Module 01-Lesson 14_Regression/06. Scatter Plots -DvlxZ37O4i8.mp4

3.5 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4

3.5 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/10. L6 131 Lesson Summary V1-t6ss31RZF34.mp4

3.5 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/16. Starring interesting repositories-ZwMY5rAAd7Q.mp4

3.5 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/18. Feature Extraction-UgENzCmfFWE.mp4

3.5 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/16. Inheritance Gaussian Class-XS4LQn1VA3U.mp4

3.5 MB

Part 12-Module 01-Lesson 16_Logistic Regression/33. Congrats-Qy8VYdqoxGA.mp4

3.5 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/02. Identifying Recommendation Engines-KwegrgvV-V4.mp4

3.5 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/29. Conclusion-zX5jZH2y8d8.mp4

3.5 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png

3.5 MB

Part 12-Module 01-Lesson 06_Conditional Probability/09. Medical Example 8-btGdX0ZpkNU.mp4

3.5 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 0424 V1-x-End5px36M.mp4

3.5 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/10. Minimum-MEbJxfw3NVs.mp4

3.5 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/27. Dummy Variables-bgxBUvPpKQQ.mp4

3.5 MB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/03. Further Motivation-sjGxUKrbKoI.mp4

3.5 MB

Part 10-Module 01-Lesson 01_What is Version Control/06. Onward-iXbMaTwfIJI.mp4

3.5 MB

Part 06-Module 01-Lesson 07_Pandas/06. Pandas 3 V1-yhMT0X6YPFA.mp4

3.5 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/14. Inheritance-1gsrxUwPI40.mp4

3.5 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/06. Example of Sampling Distributions - Part I-1XezzP6kxUE.mp4

3.5 MB

Part 04-Module 01-Lesson 01_Clustering/12. How Does K-Means Work-pL-pMCDgJuw.mp4

3.5 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/16. 04 Inline Comments V1--G6yg3Xhl8I.mp4

3.5 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/13. Error Functions-YfUUunxWIJw.mp4

3.5 MB

Part 20-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4

3.5 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/01. Introduction to Data Cleaning-YTtH3NM2BX0.mp4

3.6 MB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L5 26 Branching Overview-ywcOC6CLG4s.mp4

3.6 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/08. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4

3.6 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/08. Example of Sampling Distributions - Part 3-E_4lvTWkSNI.mp4

3.6 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/16. Creating Custom Transformers-TBxUCQdXRjY.mp4

3.6 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. Data Vis L4 C02 V1-wBDC5AmYgyg.mp4

3.6 MB

Part 12-Module 01-Lesson 07_Bayes Rule/18. Disease Test 5-nUxwwMNKIYo.mp4

3.6 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/14. Performance Tuning Motivation-aY4_uYWEuoE.mp4

3.6 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/11. CAST-LbyOq4ofLng.mp4

3.6 MB

Part 12-Module 01-Lesson 07_Bayes Rule/14. Disease Test 1-qDGSvvabN18.mp4

3.6 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4

3.6 MB

Part 10-Module 01-Lesson 01_What is Version Control/04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.mp4

3.6 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/15. L3 141 Lesson Summary V1-7ZaSMbsJUWU.mp4

3.6 MB

Part 07-Module 01-Lesson 01_Basic SQL/09. Types Of Statements-vLvJbIz94C4.mp4

3.6 MB

Part 15-Module 01-Lesson 06_Web Development/02. L4 Lesson Overview V2-9WQF-CCNdJ8.mp4

3.6 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/06. L6 061 Polishing Plots V3-4TixzVx79uk.mp4

3.6 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/20. Calculating the p-value-_W3Jg7jQ8jI.mp4

3.6 MB

Part 12-Module 01-Lesson 04_Probability/18. Doubles-On_Guw8wac8.mp4

3.6 MB

Part 12-Module 01-Lesson 04_Probability/13. One Head 1-lHuZpDkfwq8.mp4

3.7 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.7 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.7 MB

Part 20-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.7 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-36KLIHioAvA.mp4

3.7 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators Motivation-ClzbfQyhNro.mp4

3.7 MB

Part 12-Module 01-Lesson 14_Regression/16. How Do We Interpret Results-eLk0XGGMaCE.mp4

3.7 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/02. Window Functions-gp0RPgkDHsQ.mp4

3.7 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/16. Confidence Intervals And Hypothesis Tests-T2d9AUnWl-I.mp4

3.7 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/04. Good GitHub repository-qBi8Q1EJdfQ.mp4

3.7 MB

Part 09-Module 01-Lesson 01_Shell Workshop/15. Ud206 020 Shell P13 Controlling The Shell Prompt ($PS1)-nnqvRZ-Fx3k.mp4

3.7 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/03. Sampling Distributions Confidence Intervals-gICzUhMVymo.mp4

3.7 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. Data Vis L4 C09 V1-OnzWhpgM9Vs.mp4

3.7 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. Data Vis L4 C13 V1-Z7NjwA6jbjU.mp4

3.7 MB

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-GlmS_jox08s.mp4

3.7 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. Data Vis L4 C03 V1-0F6ldBC6Nbs.mp4

3.8 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/17. Simulating From the Null-sL2yJtHZd8Y.mp4

3.8 MB

Part 20-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4

3.8 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4

3.8 MB

Part 14-Module 01-Lesson 01_The Data Science Process/19. The Data Science Process Modeling-bzR6HQBn5CA.mp4

3.8 MB

Part 06-Module 01-Lesson 07_Pandas/05. Pandas 2 V1-B7MuFIwboKU.mp4

3.8 MB

Part 10-Module 01-Lesson 01_What is Version Control/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.mp4

3.8 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/18. Data in the Real World-HmipezTjTDY.mp4

3.8 MB

Part 12-Module 01-Lesson 07_Bayes Rule/01. Bayes Rules-CohZnkZMOxE.mp4

3.8 MB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/07. Outro-ot4fPX1jzOI.mp4

3.8 MB

Part 06-Module 01-Lesson 07_Pandas/04. Pandas 1 V1-iXnYN8cnhzs.mp4

3.8 MB

Part 07-Module 01-Lesson 06_[Advanced] SQL Window Functions/01. Introduction to Window Functions-u3qLjP8KMKc.mp4

3.8 MB

Part 04-Module 01-Lesson 04_PCA/02. Lesson Topics-LBzA08F_r4w.mp4

3.8 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/04. L3 Git And Github WalkThrough V1-buMNfXkj9fg.mp4

3.8 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/08. Continuous vs. Discrete Data-BzgZebZD9kk.mp4

3.8 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/26. GloVe-KK3PMIiIn8o.mp4

3.8 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/15. The Median-WlT3eeW0rb0.mp4

3.8 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/13. L4 131 Line Plots V1-kSntEWPuOa0.mp4

3.8 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/05. Cleaning With More Advanced String Functions-E6cK8RbYGEc.mp4

3.8 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/02. NULLs-WYUkLKn6XCw.mp4

3.8 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/34. Scaling Data-OgjTk3XCUUE.mp4

3.8 MB

Part 12-Module 01-Lesson 08_Python Probability Practice/02. Simulating Coin Flips-7YtQNZ3iy6o.mp4

3.8 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/03. A Little More History - A Computer Scientist's Perspective-sVT9nX6HTyU.mp4

3.8 MB

Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4

3.8 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4

3.8 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/22. Outliers Advice-BhhDoTgYQmI.mp4

3.8 MB

Part 20-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4

3.8 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4

3.8 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4

3.8 MB

Part 02-Module 01-Lesson 02_Linear Regression/13. Minimizing Error Functions-RbT2TXN_6tY.mp4

3.8 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/01. L3 011 Intro V3-4BpAF4MYKm8.mp4

3.9 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/25. DATE Functions I-E7Z6GMFVmIY.mp4

3.9 MB

Part 04-Module 01-Lesson 04_PCA/18. 17 PCA Recap V1-Egz3-noHCmg.mp4

3.9 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/07. Example of Sampling Distributions - Part II-PKf3Nu6zAxM.mp4

3.9 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/11. Traditional vs. Bootstrapping Confidence Intervals-eZ8lyiumXDY.mp4

3.9 MB

Part 02-Module 01-Lesson 02_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4

3.9 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/07. Metric - Click Through Rate-EpfoKAwV_Eg.mp4

3.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/12. Types Of Collaborative Filtering-fZhkWHHP6SM.mp4

3.9 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/01. L6 011 Intro V1-gLy8qpursJI.mp4

3.9 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/16. How Do We Choose Between Hypotheses-JkXTwS-5Daw.mp4

3.9 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.mp4

3.9 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/12. Types Of Errors - Part III-Z-srkCPsdaM.mp4

3.9 MB

Part 07-Module 01-Lesson 01_Basic SQL/06. Why Do Analysts Like SQL-uCNOtUht2Xc.mp4

3.9 MB

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-M3L0a5V4Nf0.mp4

3.9 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4

4.0 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/13. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4

4.0 MB

Part 07-Module 01-Lesson 02_SQL Joins/02. Why Use Separate Tables-UIQBtpmqYOs.mp4

4.0 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/27. L2 04 Tuples V3-33xN-AbTMoc.mp4

4.0 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/02. Lesson Overview -q1beUVlLoIQ.mp4

4.0 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/02. First Things First-ehjC7JK-zMI.mp4

4.0 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/12. Introduction to Summary Statistics-PCZmHCrcMcw.mp4

4.0 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/15. Conclusions-3IFF1GzUq0Y.mp4

4.0 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/07. Quick Fixes-Lb9e2KemR6I.mp4

4.0 MB

Part 02-Module 01-Lesson 10_Finding Donors Project/01. ML Charity Project-aVodYHcOB8U.mp4

4.0 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/06. Course Wrap Up-66Ut8Bv6kgc.mp4

4.0 MB

Part 14-Module 01-Lesson 01_The Data Science Process/02. CRISP-DM-PaVwnGcqlSE.mp4

4.0 MB

Part 12-Module 01-Lesson 04_Probability/08. Two Flips 1-1txkcmxk3vU.mp4

4.0 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/19. Bag Of Words-A7M1z8yLl0w.mp4

4.0 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4

4.0 MB

Part 20-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4

4.0 MB

Part 14-Module 01-Lesson 01_The Data Science Process/24. Working With Missing Values-mbAgYicmzqE.mp4

4.0 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/12. SVD Practice Takeaways-2er0HUDum7k.mp4

4.0 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/07. L4 071 Box Plots V4-3gxJag12T0g.mp4

4.0 MB

Part 06-Module 01-Lesson 01_Why Python Programming/03. L1 03 Programming In Python V4-O1cTNYAjeeg.mp4

4.0 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/02. L2 2 02 Testing V1 V1-IkLUUHt_jis.mp4

4.1 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/23. Bootstrapping the Central Limit Theorem-GJGUwNr_82s.mp4

4.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/39. Load Walk Through-AZvC7kYp_74.mp4

4.1 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/26. Why Are Sampling Distributions Important-aDFDOCJKoH0.mp4

4.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/43. Outro V1 V4-XE3aoYOXeBw.mp4

4.1 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/08. Know Your Audience-OjmrU5HlFD8.mp4

4.1 MB

Part 10-Module 01-Lesson 07_Working With Remotes/02. L1 - Remote Repos Intro-AnSlYftJnwA.mp4

4.1 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/06. DataVis L5C06 V2-BzzTlWHMyV0.mp4

4.1 MB

Part 14-Module 01-Lesson 01_The Data Science Process/41. How To Fix This-IPQZ4pfRMRA.mp4

4.1 MB

Part 20-Module 01-Lesson 01_Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4

4.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/22. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4

4.1 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/15. Two Useful Theorems-jQ5i7CALdRQ.mp4

4.2 MB

Part 02-Module 01-Lesson 04_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4

4.2 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/12. L4 121 Adaptations Of Univariate Plots V3-MXcqplnUB0o.mp4

4.2 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/04. Business Example-Wzz7omSDfEk.mp4

4.2 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/14. Other Sampling Distributions-Bxl0DonzX8c.mp4

4.2 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/01. L5 011 Intro V3-ckylQMBXB10.mp4

4.2 MB

Part 12-Module 01-Lesson 16_Logistic Regression/27. Equation for Precision-8QEAYYIyopY.mp4

4.2 MB

Part 07-Module 01-Lesson 02_SQL Joins/01. Introduction to JOINs-YvZ010GU-Ck.mp4

4.2 MB

Part 02-Module 01-Lesson 04_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4

4.2 MB

Part 12-Module 01-Lesson 14_Regression/17. How Do We Know If Our Model Fits Well-0vPtPAqMHJE.mp4

4.2 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/35. L2 01 Compound Data Structures V1-jmQ8IKvQgBU.mp4

4.2 MB

Part 12-Module 01-Lesson 04_Probability/01. Introduction to Probability-HeoQccoqfTk.mp4

4.2 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/05. JOINs with Comparison Operators-48AgxPygRuQ.mp4

4.2 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/08. Dropout-Ty6K6YiGdBs.mp4

4.2 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4

4.2 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/09. L4 091 Clustered Bar Charts V4-0rFp55TtEJM.mp4

4.2 MB

Part 07-Module 01-Lesson 05_SQL Data Cleaning/02. Cleaning with String Functions-y1fduSu7Ovc.mp4

4.2 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. DataVis L5C03 V2-iokI7HrxeNc.mp4

4.2 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4

4.2 MB

Part 20-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4

4.2 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.mp4

4.2 MB

Part 04-Module 01-Lesson 01_Clustering/16. Feature Scaling-rpTVp7C8AXo.mp4

4.2 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/22. Bootstrapping-42j3YclcZ4Q.mp4

4.2 MB

Part 06-Module 01-Lesson 05_Scripting/01. Scripting-Qxe_gCiXUDg.mp4

4.2 MB

Part 06-Module 01-Lesson 05_Scripting/13. Handling Errors Try Except Finally-S6hwBZG0KwM.mp4

4.2 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. Performance Tuning 1-5mVfYZ_bfRo.mp4

4.2 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/01. Introduction-TRw4bvZuEG8.mp4

4.3 MB

Part 02-Module 01-Lesson 02_Linear Regression/09. Gradient Descent-4s4x9h6AN5Y.mp4

4.3 MB

Part 12-Module 01-Lesson 07_Bayes Rule/05. Normalizing 2-WYA5Zbf8HC4.mp4

4.3 MB

Part 12-Module 01-Lesson 16_Logistic Regression/20. Precision and Recall-3vT0kSBCLdU.mp4

4.3 MB

Part 04-Module 01-Lesson 01_Clustering/01. Introduction-k7YOVTkFRJM.mp4

4.3 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/02. L3 02 Proced Vs OOP V1 V3-psXD_J8FnCQ.mp4

4.3 MB

Part 02-Module 01-Lesson 04_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4

4.3 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/09. What's Ahead-2Hxy2Jlu8nk.mp4

4.3 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/04. L4 041 Heat Maps V4-RyCdvsmBjtE.mp4

4.3 MB

Part 04-Module 01-Lesson 01_Clustering/15. Is That The Optimal Solution-g5aPtCpBNmw.mp4

4.3 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/04. DataVis L3 04 V2-HLum_ys7RJ0.mp4

4.3 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/24. Binomial Class-xTamXY6Z9Kg.mp4

4.3 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/03. Your First Subquery-cTM1jPYXLoQ.mp4

4.3 MB

Part 04-Module 01-Lesson 01_Clustering/08. Elbow Method For Finding K-e7fqXpo63n8.mp4

4.3 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.mp4

4.3 MB

Part 06-Module 01-Lesson 06_NumPy/03. NumPy 0 V1-vyjMs8KFHlE.mp4

4.3 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/12. L5 121 Lesson Summary V1-SOBCduyymkQ.mp4

4.4 MB

Part 12-Module 01-Lesson 16_Logistic Regression/22. Bush Precision and Recall-FLpXmoHp7eE.mp4

4.4 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. Magic Methods in Code-oDuXThOqans.mp4

4.4 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/04. 29 Number Summary-gzUN5zKLHjQ.mp4

4.4 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/32. Hypothesis Testing Conclusion-nQFchD4XPPs.mp4

4.4 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. L5 021 Non Positional Encodings For Third Variables V1-D91mm-qaDkk.mp4

4.4 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/15. L2 10 Documentation V1 V3-M45B2VbPgjo.mp4

4.4 MB

Part 04-Module 01-Lesson 01_Clustering/13. 14 How Does KMeans Work V1-y7yZyyHgyYU.mp4

4.4 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/07. A Repository's History - Outro-9rUf2HbdAd8.mp4

4.4 MB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.mp4

4.4 MB

Part 12-Module 01-Lesson 07_Bayes Rule/32. Reducing Uncertainty-zuFMhmKQ--o.mp4

4.4 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4

4.4 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing Introduction-mRbeT2XVL9w.mp4

4.4 MB

Part 09-Module 01-Lesson 01_Shell Workshop/02. Ud206 002 P0 Windows Installing Git Bash-UQZvV6VTlGQ.mp4

4.4 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/11. 19 Transform Intro V2 V3-SXp4Qa-rQJg.mp4

4.4 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/13. L3 10 Captivate Your Audience Now What V1-Iy08sZYuqkI.mp4

4.4 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/08. Ethics in ML-fNcTTXR8T08.mp4

4.4 MB

Part 15-Module 01-Lesson 06_Web Development/08. IDs and Classes-jnfDqdxDbO4.mp4

4.4 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/15. Binomial 6-n_OrWrZ8tKY.mp4

4.4 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/img/business-money-pink-coins.jpg

4.5 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/22. Random Observed Values-KFIt2OC3wCI.mp4

4.5 MB

Part 06-Module 01-Lesson 04_Functions/18. Conclusion-QRnLr7pwHyk.mp4

4.5 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/04. 06 Unit Tests V1-wb9jggHEvgI.mp4

4.5 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/19. Organizing Code Into Modules-AARS10U5bbo.mp4

4.5 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/03. L5 031 Color Palettes V1-nirOTWkuiSM.mp4

4.5 MB

Part 12-Module 01-Lesson 04_Probability/07. Complementary Outcomes-YseJqD-1oUg.mp4

4.5 MB

Part 07-Module 01-Lesson 01_Basic SQL/24. WHERE Statements -mN0uTnlXaxg.mp4

4.5 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/16. Drawing Conclusions-s-4ghG9vrGQ.mp4

4.5 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/17. Other Important Information-LF-CWF-1mX4.mp4

4.5 MB

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-tI0J14yQr1s.mp4

4.5 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/12. Combining Data From Different Sources-IfMydJvU37M.mp4

4.5 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt 2-0qcJ_oggdKw.mp4

4.5 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/11. SMART Mnemonic-B0Bnxyu2aKM.mp4

4.5 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/13. Using Feature Unions-QmE6CMGar1U.mp4

4.6 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/26. DATE Functions Part II-UPWkDhW4cLI.mp4

4.6 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/26. Conclusion-R5-OYqKk9Ys.mp4

4.6 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/02. L3 - Pull Request In Action-d3AGtKmHxUk.mp4

4.6 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/26. Recap-VM6GGNC2q8I.mp4

4.6 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/12. L3 111 Descriptive Stats Outliers And Axis Limits V2-kQoK7UwrGh0.mp4

4.6 MB

Part 12-Module 01-Lesson 06_Conditional Probability/11. Two Coins 1-QIQBb4nLsHc.mp4

4.6 MB

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-lgUDXtUyLLg.mp4

4.6 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/11. L4 111 Faceting V2-oUYRqI6wFGw.mp4

4.6 MB

Part 14-Module 01-Lesson 01_The Data Science Process/27. Removing Data - Other Considerations-xrXk_Tvi0oQ.mp4

4.6 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/08. Checking Validity-H3H1SZXqDmQ.mp4

4.6 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/13. Parting Words Of Encouragement-sFF_WOnpsXM.mp4

4.7 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/10. Parting Words Of Encouragement-sFF_WOnpsXM.mp4

4.7 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/03. L4 031 Overplotting Transparency And Jitter 1 V4-BGqR-nxgMtg.mp4

4.7 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/05. Quadratics 2-HjpgML5zsUE.mp4

4.7 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/07. Aggregation-55eZrE82TqA.mp4

4.7 MB

Part 12-Module 01-Lesson 16_Logistic Regression/14. Confusion Matrix False Alarms-Uf_KdjVT2Xg.mp4

4.7 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/02. Sampling To Distributions To Confidence Intervals-QYMLkDToigc.mp4

4.7 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-Jp2xJOtNQZ0.mp4

4.7 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/04. Same Data Different Stories-jSSnkz3QT5Y.mp4

4.7 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/07. Controlling Variables-pLTneSg2MRY.mp4

4.7 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/27. Embeddings For Deep Learning-gj8u1KG0H2w.mp4

4.7 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/02. Corporate Messaging Case Study-xnDsUsrF884.mp4

4.7 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/20. Content Based Recommendations-pnGHpB77Mys.mp4

4.7 MB

Part 04-Module 01-Lesson 04_PCA/01. Introduction-tpFPcxoGxaE.mp4

4.7 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/32. Combinando modelos-Boy3zHVrWB4.mp4

4.7 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4

4.7 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt1-cWB1jQgcQ1g.mp4

4.8 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/03. Fitting A Multiple Linear Regression Model-EZNvBF66_b0.mp4

4.8 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/23. Types Of Recommendations-uoXF81AO21E.mp4

4.8 MB

Part 02-Module 01-Lesson 04_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4

4.8 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/20. When Does the CLT Not Work-uZGTVUEMfrU.mp4

4.8 MB

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-cDub-OOrIRE.mp4

4.8 MB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.mp4

4.8 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/19. What is Notation-MaHV5cKfcmE.mp4

4.8 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 07 Lists And Membership Operators II V3-3Nj-b-ZzqH8.mp4

4.8 MB

Part 20-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4

4.8 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/23. Error Function-V5kkHldUlVU.mp4

4.8 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/13. Measuring SImilarity-G_Y6IPmp7Xs.mp4

4.8 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/13. L3 121 Scales And Transformations V3-PE53ga2bOME.mp4

4.8 MB

Part 14-Module 01-Lesson 02_Communicating to Stakeholders/11. Captivate Your Audience - First Catch Their Eye-lO8-YKgW7y0.mp4

4.9 MB

Part 11-Module 01-Lesson 02_Vectors/01. Vectors 1-oPBz-MLVUHk.mp4

4.9 MB

Part 15-Module 01-Lesson 06_Web Development/04. The Front End-CspuxLGFM4U.mp4

4.9 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/15. Designing for Color Blindness-k4iTzS7t2U4.mp4

4.9 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/21. Working with Outliers-4RnQjtJB8t8.mp4

4.9 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/05. Model Complexity Graph-NnS0FJyVcDQ.mp4

4.9 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4

4.9 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/01. Binomial-3koDdc9r73E.mp4

4.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 7251010 V1-sVZ5S1nnRf8.mp4

4.9 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/15. Stemming And Lemmatization-7Gjf81u5hmw.mp4

4.9 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/03. How Do We Know Our Recs Are Good-D0H_fjJ35CU.mp4

4.9 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/12. L3 10 Magic M V1 V3-9dEsv1aNUEE.mp4

5.0 MB

Part 10-Module 01-Lesson 04_Add Commits To A Repo/07. Outro-5eyvsMvAPYs.mp4

5.0 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 0435 V1-oRhrOShUM6w.mp4

5.0 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/22. 30 Imputing Missing Data V1 V3-A5sOJDj3AKg.mp4

5.0 MB

Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules Part II-aASigWQ_XU0.mp4

5.0 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/14. Central Limit Theorem-9I8ysrRlmbA.mp4

5.0 MB

Part 10-Module 01-Lesson 01_What is Version Control/03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.mp4

5.0 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/04. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4

5.0 MB

Part 14-Module 01-Lesson 01_The Data Science Process/45. The Data Science Process Evaluate And Deploy-sxT43JlH_eM.mp4

5.0 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4

5.1 MB

Part 07-Module 01-Lesson 01_Basic SQL/11. SELECT FROM Statements-urOYuuav4BY.mp4

5.1 MB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/01. L1 011 Data Visualization In Data Analysis Intro V3 V3-U1VapEELBfw.mp4

5.1 MB

Part 04-Module 01-Lesson 04_PCA/13. 12 Interpret PCA Results V1-ZX6EACfsZbc.mp4

5.1 MB

Part 14-Module 01-Lesson 01_The Data Science Process/25. Removing Data - Why Not-w3-5Z5mEzTM.mp4

5.1 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/03. Setting Up Hypotheses - Part I-NpZxJg4S6X4.mp4

5.1 MB

Part 06-Module 01-Lesson 06_NumPy/09. NumPy 5 V1-vGjI-WTnEbY.mp4

5.1 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/25. Aggregations-ADx1x2ljFB4.mp4

5.1 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/12. Conclusions-yMRRXDKb428.mp4

5.1 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

5.1 MB

Part 20-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

5.1 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

5.1 MB

Part 02-Module 01-Lesson 05_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4

5.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/36. 44 Feature Engineering V1 V1-7Bof5l8xjz8.mp4

5.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/25. Duplicate Data-49ZwWRviAFg.mp4

5.2 MB

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1--TBAfU1cjRU.mp4

5.2 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/08. DataVis L3 08 V2-f1we_0dUSXg.mp4

5.2 MB

Part 02-Module 01-Lesson 02_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4

5.2 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/13. Early Stopping-taIJZMNwRsI.mp4

5.2 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/05. Measuring Outcomes Pt 2-yLdXcRXcfPw.mp4

5.2 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/07. What is the Standard Deviation Measuring-IbwUJ3ORZ5s.mp4

5.2 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4

5.2 MB

Part 06-Module 01-Lesson 01_Why Python Programming/02. L1 01 Intro V3-yyNtiUyI5Tw.mp4

5.2 MB

Part 12-Module 01-Lesson 07_Bayes Rule/35. Using Sensor Data-vhl-SADfti8.mp4

5.2 MB

Part 04-Module 01-Lesson 04_PCA/08. PCA Properties-1oaaq-0wdB0.mp4

5.2 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/12. Common Table Expressions-qtEKO7B8bXQ.mp4

5.2 MB

Part 06-Module 01-Lesson 05_Scripting/08. Scripting With Raw Input-Fs9uLV2qfgI.mp4

5.2 MB

Part 12-Module 01-Lesson 04_Probability/19. Probability Conclusion-dsVKoXymYDU.mp4

5.3 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/14. 22 Cleaning Data V1 V3-zYxgkUqTX0Y.mp4

5.3 MB

Part 07-Module 01-Lesson 01_Basic SQL/02. Parch Posey Database-JOMI560DgXg.mp4

5.3 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/02. L4 021 Scatterplots And Correlation V2-wqMwTDVT9_Y.mp4

5.3 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 05 Lists Methods V1-WXkPm4rv6ng.mp4

5.3 MB

Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. Forking a Repository - What Is Forking-z4mkVwqVztc.mp4

5.3 MB

Part 15-Module 01-Lesson 06_Web Development/18. L4 The Back End V2-Esl0NL63S2c.mp4

5.3 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/12. Variance Standard Deviation Final Points-vXUgp2375j4.mp4

5.3 MB

Part 07-Module 01-Lesson 02_SQL Joins/13. Motivation for Other JOINs-3qdv1Ojc9Og.mp4

5.3 MB

Part 06-Module 01-Lesson 05_Scripting/24. Techniques For Importing Modules-jPGyFgcIvsM.mp4

5.3 MB

Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/02. Disaster Relief Project Preview-DuwYAjqGM3E.mp4

5.3 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4

5.3 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/33. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4

5.3 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/05. Extract Walk Through-Bbj8rQRRVoM.mp4

5.3 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/01. Introduction-5DfFaAl1Wmc.mp4

5.3 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4

5.3 MB

Part 20-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4

5.3 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/09. Chart Junk-3BTBEYOG2o8.mp4

5.4 MB

Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-GO6kbL3QRBE.mp4

5.4 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/23. Putting It All Together-r5jfD2uKnbQ.mp4

5.4 MB

Part 12-Module 01-Lesson 14_Regression/13. Fitting A Regression Line-xQob80zrT3s.mp4

5.4 MB

Part 02-Module 01-Lesson 03_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4

5.4 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/25. Background Of Bootstrapping-6Vg5kGoDl7k.mp4

5.4 MB

Part 08-Module 01-Lesson 04_Bivariate Exploration of Data/15. L4 151 Lesson Summary V1-5igqM44KEmw.mp4

5.4 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 1 Advantages Of Using Pipeline V1 V2-ASYcx911E2Q.mp4

5.4 MB

Part 02-Module 01-Lesson 09_Training and Tuning/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4

5.4 MB

Part 02-Module 01-Lesson 05_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4

5.4 MB

Part 02-Module 01-Lesson 04_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4

5.4 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4

5.4 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/31. 38 Outliers What To Do With Them V1 V2-Yd_fPCmGNZ0.mp4

5.4 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/13. Calculating the Mean-1nzZxmJ8xvU.mp4

5.5 MB

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-gGgqTGZ9TKg.mp4

5.5 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 0425 V1-vPpX7ITgb3g.mp4

5.5 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/02. History - Statisticians Perspective-zNNouqLGF9E.mp4

5.5 MB

Part 14-Module 01-Lesson 01_The Data Science Process/04. Business And Data Understanding - Example-bXQTGS61BU8.mp4

5.5 MB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/06. L1 061 Visualization In Python V1-MFS-1veFC_c.mp4

5.5 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/06. Creating Metrics-__7tzDUY870.mp4

5.5 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/01. Intro-EBGMcpWe8-U.mp4

5.5 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4

5.5 MB

Part 01-Module 04-Lesson 01_What Is Ahead/05. Outro-xj70jX9Moxs.mp4

5.5 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/06. Outro-xj70jX9Moxs.mp4

5.5 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/08. DataVis L5C08 V2-fq-hakwfpZw.mp4

5.5 MB

Part 12-Module 01-Lesson 07_Bayes Rule/23. Robot Sensing 3-m1LSU9SPZ2k.mp4

5.6 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/28. T-SNE-xxcK8oZ6_WE.mp4

5.6 MB

Part 06-Module 01-Lesson 05_Scripting/27. Experimenting With An Interpreter-hspPtnQwMPg.mp4

5.6 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/06. Why SVD-WdW1-rRQrLk.mp4

5.6 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/01. 01 Intro V1 2 V4-iW4uqhfRk10.mp4

5.6 MB

Part 07-Module 01-Lesson 01_Basic SQL/15. LIMIT Statement-cCPHNNhBgpQ.mp4

5.6 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/03. Testing-gmxGRJSKEb0.mp4

5.6 MB

Part 06-Module 01-Lesson 04_Functions/02. Default Arguments-cG6UfBZX2KI.mp4

5.6 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/01. L2 01 Intro V1 V1-z7v7oa--W48.mp4

5.6 MB

Part 12-Module 01-Lesson 01_Descriptive Statistics - Part I/20. Notation for Random Variables-8NxTW1u4s-Y.mp4

5.7 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 09 Lists And Membership Operators V2-rNV_E50wcWM.mp4

5.7 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4

5.7 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4

5.7 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/20. The Cold Start Problem-DNz7aywJVzA.mp4

5.7 MB

Part 12-Module 01-Lesson 04_Probability/17. Even Roll-DrnAR4SqlEE.mp4

5.7 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/08. Using Pipelines-mxFrS8qpZ6Y.mp4

5.7 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 26423140 V1-uNQHtPrfi4o.mp4

5.7 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/13. GROUP BY-9vb67TF4WV0.mp4

5.7 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/20. Outliers-HKIsvkZUZfo.mp4

5.7 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4

5.7 MB

Part 20-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4

5.7 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/19. 15 Pipelines And Grid Search V1 V3-HZaOiSxJjCY.mp4

5.7 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/26. Types Of Ratings-fMjqe4sxBlQ.mp4

5.8 MB

Part 14-Module 01-Lesson 01_The Data Science Process/03. The Data Science Process Business And Data Understanding-eG_jKQezhc4.mp4

5.8 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4

5.8 MB

Part 12-Module 01-Lesson 04_Probability/03. Fair Coin-9LrlrexpW_o.mp4

5.8 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/09. Checking Bias-ppjNNY4DhPw.mp4

5.8 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4

5.9 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4

5.9 MB

Part 07-Module 01-Lesson 01_Basic SQL/01. Introduction-Z8WNfx9Oq9s.mp4

5.9 MB

Part 15-Module 01-Lesson 06_Web Development/01. L4 Intro V2--PGMIIXFCgg.mp4

5.9 MB

Part 07-Module 01-Lesson 02_SQL Joins/18. JOINs and Filtering-aI1kbDDNs4w.mp4

5.9 MB

Part 06-Module 01-Lesson 03_Control Flow/01. Introduction-eUrvACMMJ5w.mp4

5.9 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/07. L2 2 09 Test Driven Development DS V1 V2-M-eskssLcQM.mp4

5.9 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/16. Shape, Size, and other Tools-fzEliHW3ZLM.mp4

5.9 MB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files Part II-1GRv1S6K8gQ.mp4

5.9 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/29. L2 03 Sets V2-eIHNFgTFfnA.mp4

5.9 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/41. Putting It All Together-PHaSifd-Mas.mp4

5.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/16. Identifying Recommendations-P60qvS_OTMg.mp4

6.0 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/08. What Experts Say About Visual Encodings-98aog0eVcC4.mp4

6.0 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/07. Using Dummy Tests-rURTLjh3Hlc.mp4

6.0 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.mp4

6.0 MB

Part 02-Module 01-Lesson 09_Training and Tuning/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4

6.0 MB

Part 04-Module 01-Lesson 05_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4

6.0 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/09. Gaussian Class-TVzNdFYyJIU.mp4

6.0 MB

Part 14-Module 01-Lesson 01_The Data Science Process/26. Removing Data - When Is It OK-oQhIPq5AccU.mp4

6.0 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/13. 08 F1 Score SC V1-TRzBeL07fSg.mp4

6.0 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/03. Tell A Story-_IdOUEhjVGI.mp4

6.1 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/07. L3 071 Pie Charts V3-kSrJGJHTKV8.mp4

6.1 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/22. L2 08 Lists And Membership Operators V2-JAbZdZg5_x8.mp4

6.1 MB

Part 17-Module 02-Lesson 02_Statistical Considerations in Testing/05. Experiment Size-sImRm8e01jA.mp4

6.1 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/07. Confidence Intervals Applications-C0wgmeRx9yE.mp4

6.1 MB

Part 02-Module 01-Lesson 05_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4

6.1 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/02. What Makes a Bad Visual-zbvB_9f7bFs.mp4

6.1 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/29. L3 21 Outro v1 V2-DStO1hBKtHQ.mp4

6.2 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/02. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4

6.2 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/27. Descriptive vs. Inferential Statistics-XV9pd8-RZ78.mp4

6.2 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4

6.2 MB

Part 08-Module 01-Lesson 06_Explanatory Visualizations/08. Data Vis L6 C06 V1-qIot9qrvcF8.mp4

6.2 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.mp4

6.2 MB

Part 18-Module 01-Lesson 01_Data Scientist Capstone/01. Capstone-jewlarqqbTo.mp4

6.2 MB

Part 15-Module 01-Lesson 06_Web Development/22. Flask and Pandas-L_M_8UVY42k.mp4

6.2 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/01. Introduction-Yg0gBpTzkMo.mp4

6.2 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/10. Binomial 1-07vOaYwecII.mp4

6.3 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/06. Notes On OOP-NcgDIWm6iBA.mp4

6.3 MB

Part 12-Module 01-Lesson 07_Bayes Rule/29. Generalizing-SdMk3aROgSc.mp4

6.3 MB

Part 11-Module 01-Lesson 02_Vectors/03. Vectors 3-mWV_MpEjz9c.mp4

6.3 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/12. Other Language Associated With Confidence Intervals-9KYVRx7-llg.mp4

6.3 MB

Part 10-Module 01-Lesson 06_Undoing Changes/01. Undoing Changes - Intro-Kfi7l41wUVc.mp4

6.3 MB

Part 12-Module 01-Lesson 13_Case Study AB tests/02. AB Testing-EcWvhbIjT9o.mp4

6.3 MB

Part 02-Module 01-Lesson 04_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4

6.3 MB

Part 12-Module 01-Lesson 10_Sampling distributions and the Central Limit Theorem/12. Notation Parameters vs. Statistics-webref_dLrA.mp4

6.3 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 0422 V1-J4MOXJhMGGA.mp4

6.3 MB

Part 19-Module 01-Lesson 01_Congratulations!/01. Congrats-OTp4YOTDd0Q.mp4

6.4 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/01. Introduction-RVcFzwBXI2M.mp4

6.4 MB

Part 02-Module 01-Lesson 01_Machine Learning Bird's Eye View/06. Deep Learning And Neural Networks-4rKw3ekE5Wk.mp4

6.4 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. L3 031 Bar Charts V3-ybXcduB6cXA.mp4

6.4 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.mp4

6.4 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/04. Underfitting And Overfitting-xj4PlXMsN-Y.mp4

6.4 MB

Part 02-Module 01-Lesson 10_Finding Donors Project/07. Project 1-PNsxDWtpQTk.mp4

6.4 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 31423505 V1-A0uOjClDnW8.mp4

6.5 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/11. Design Integrity-y72_fVFtqlY.mp4

6.5 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.mp4

6.5 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4

6.5 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/34. Backpropagation V2-1SmY3TZTyUk.mp4

6.5 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/11. Why the Standard Deviation-XlTBvjQ2t8w.mp4

6.5 MB

Part 12-Module 01-Lesson 06_Conditional Probability/01. Introduction to Conditional Probability-Ok8948Wcbmo.mp4

6.5 MB

Part 14-Module 01-Lesson 01_The Data Science Process/18. It Is Not Always About ML-ECqflypBU7M.mp4

6.5 MB

Part 02-Module 01-Lesson 09_Training and Tuning/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4

6.6 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/08. Maximum-02v8ui9riew.mp4

6.6 MB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.mp4

6.6 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4

6.6 MB

Part 20-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4

6.6 MB

Part 06-Module 01-Lesson 06_NumPy/11. NumPy 6 V1-wtLRuGK0kW4.mp4

6.6 MB

Part 12-Module 01-Lesson 06_Conditional Probability/04. Medical Example 3-Iz4ViIg9ZlQ.mp4

6.6 MB

Part 18-Module 01-Lesson 01_Data Scientist Capstone/09. Capstone-bq-H7M5BU3U.mp4

6.6 MB

Part 02-Module 01-Lesson 08_Model Evaluation Metrics/15. ROC Curve-2Iw5TiGzJI4.mp4

6.7 MB

Part 15-Module 01-Lesson 06_Web Development/24. Flask Pandas Plotly Part 1-xg7P8MnItdI.mp4

6.7 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/15. Summary-VP-PMcgqhc8.mp4

6.7 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/02. Python Installation-2_P05aYChqQ.mp4

6.7 MB

Part 06-Module 01-Lesson 05_Scripting/02. Python Installation-2_P05aYChqQ.mp4

6.7 MB

Part 12-Module 01-Lesson 07_Bayes Rule/11. Probability Given Test-omC0zbJyzUY.mp4

6.7 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/02. What Is An Experiment Pt 2-PYzN1usi7QY.mp4

6.8 MB

Part 12-Module 01-Lesson 02_Descriptive Statistics - Part II/08. Standard Deviation Calculation-H5zA1A-XPoY.mp4

6.8 MB

Part 14-Module 01-Lesson 01_The Data Science Process/09. Business And Data Understanding - Part 2-iInjuIgBWIo.mp4

6.8 MB

Part 08-Module 01-Lesson 05_Multivariate Exploration of Data/02. DataVis L5C02 V3-bgDNMfG9Gfs.mp4

6.8 MB

Part 10-Module 01-Lesson 02_Create A Git Repo/01. Creating New Repositories - Intro-KT163BkqIeg.mp4

6.8 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/10. More Personalized Recommendations-9l8mi7i6iW4.mp4

6.9 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4

6.9 MB

Part 06-Module 01-Lesson 07_Pandas/08. Pandas 4 V1-eMHUn9v9dds.mp4

6.9 MB

Part 08-Module 01-Lesson 03_Univariate Exploration of Data/03. DataVis L3 03 V2-srRhFrSPdvs.mp4

7.0 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/29. How Do Confidence Intervals Hypothesis Tests Compare-KEmsEViOoMA.mp4

7.0 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.mp4

7.0 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 4271048 V1-2On65U7Panw.mp4

7.0 MB

Part 16-Module 01-Lesson 04_Machine Learning Pipelines/09. 08 2 Advantages Of Using Pipelines V1 V2-eT1MS3n8fZ8.mp4

7.0 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4

7.0 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/03. L2 031 Levels Of Measurement And Types Of Data V6-3Plhn5Q4xIA.mp4

7.1 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4

7.1 MB

Part 12-Module 01-Lesson 07_Bayes Rule/19. Disease Test 6-OdVAt79eQak.mp4

7.1 MB

Part 15-Module 01-Lesson 06_Web Development/20. 22 Screencast Flask V2-i_U3O-7cymk.mp4

7.1 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/10. Jupyter-qiYDWFLyXvg.mp4

7.1 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 23242831 V1-WqNi0B_oRuA.mp4

7.1 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/12. Binomial 3-YIELbuet-ZE.mp4

7.1 MB

Part 20-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4

7.2 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/14. Error Functions-jfKShxGAbok.mp4

7.2 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/12. Normalizer-mQ_IjrtmmAk.mp4

7.2 MB

Part 02-Module 01-Lesson 05_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4

7.2 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 5451216 V1-lf2Q0AE5esk.mp4

7.3 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/19. What Is A P-value Anyway-eU6pUZjqviA.mp4

7.3 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/04. Quadratics-1R44jvxIPJY.mp4

7.3 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/28. Multiple Testing Corrections-DuMgeHrkIF0.mp4

7.3 MB

Part 14-Module 01-Lesson 01_The Data Science Process/30. Removing Data-97UTBiybYTs.mp4

7.3 MB

Part 06-Module 01-Lesson 05_Scripting/26. Third Party Libraries And Package Managers-epOze9gC6T4.mp4

7.4 MB

Part 12-Module 01-Lesson 07_Bayes Rule/09. Equivalent Diagram-aUFWZ2uJuBE.mp4

7.4 MB

Part 06-Module 01-Lesson 05_Scripting/06. Programming Environment Setup-EKxDnCK0NAk.mp4

7.4 MB

Part 01-Module 03-Lesson 01_Setting Up Your Computer/09. Programming Environment Setup-EKxDnCK0NAk.mp4

7.4 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/24. Conclusions In Hypothesis Testing-I0Mo7hcxahY.mp4

7.5 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4

7.5 MB

Part 12-Module 01-Lesson 16_Logistic Regression/09. Model Diagnostics-XsYFAtzF6e4.mp4

7.5 MB

Part 10-Module 01-Lesson 04_Add Commits To A Repo/01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.mp4

7.5 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/10. Gender Bias Revisited-dOa4Cl0wM0s.mp4

7.5 MB

Part 07-Module 01-Lesson 02_SQL Joins/14. Other JOINs-4edRxFmWUEw.mp4

7.5 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/29. 11 CASE V2-BInXuTY_FzE.mp4

7.5 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/05. 5 Flips 2 Heads-69je8wHh2mQ.mp4

7.5 MB

Part 06-Module 01-Lesson 06_NumPy/04. NumPy 1 V1-EOHW29kDg7w.mp4

7.5 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/21. 15 Making a Package v2-Hj2OBr1CGZM.mp4

7.5 MB

Part 03-Module 01-Lesson 01_Introduction to Neural Networks/02. Introduction-tn-CrUTkCUc.mp4

7.5 MB

Part 20-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4

7.5 MB

Part 12-Module 01-Lesson 16_Logistic Regression/31. Final Thoughts On Shifting To Machine Learning-YkZFjZ3Fx8A.mp4

7.5 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/03. Types Of Experiments-7ihDj4M7EiU.mp4

7.6 MB

Part 20-Module 01-Lesson 02_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4

7.6 MB

Part 03-Module 01-Lesson 03_Training Neural Networks/07. Regularization-ndYnUrx8xvs.mp4

7.6 MB

Part 12-Module 01-Lesson 16_Logistic Regression/10. Confusion Matrices-bgyN3RO2ICo.mp4

7.6 MB

Part 12-Module 01-Lesson 03_Admissions Case Study/02. Admissions 1-CLgVLQAEYw8.mp4

7.6 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/06. 02 Writing Modular Code V2-qN6EOyNlSnk.mp4

7.6 MB

Part 10-Module 01-Lesson 08_Working On Another Developer's Repository/02. L2 - Pushing To A Fork-WRgNpr19t48.mp4

7.6 MB

Part 06-Module 01-Lesson 03_Control Flow/02. Indentation-G8qUNOTHtrM.mp4

7.6 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/05. Setting Up Hypotheses - Part II-nByvHz77GiA.mp4

7.7 MB

Part 15-Module 01-Lesson 03_Software Engineering Practices Pt II/03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.mp4

7.7 MB

Part 10-Module 01-Lesson 05_Tagging, Branching, and Merging/img/ud123-l5-resolve-merge-conflict.gif

7.7 MB

Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-TtmQ7YCw_1Y.mp4

7.7 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 23313044 V1-pcaaBWbe34Y.mp4

7.8 MB

Part 17-Module 02-Lesson 01_Concepts in Experiment Design/10. Ethics In Experimentation Pt3-_HTolKktaC4.mp4

7.8 MB

Part 04-Module 01-Lesson 01_Clustering/20. 19 Feature Scaling Solution V1-xddMZP2SQ1U.mp4

7.8 MB

Part 06-Module 01-Lesson 07_Pandas/09. Pandas 5 V1-lClsJnZn_7w.mp4

7.9 MB

Part 06-Module 01-Lesson 07_Pandas/10. Pandas 6 V1-GS1kj04XQcM.mp4

7.9 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/10. Traditional Confidence Interval Methods-DmZwYHuz2eM.mp4

7.9 MB

Part 15-Module 01-Lesson 06_Web Development/25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.mp4

7.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17b 15022032 V1-N9ytffw5AMg.mp4

8.0 MB

Part 04-Module 01-Lesson 05_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4

8.0 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4

8.0 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/18. Matching Encodings-398xRMnhjGk.mp4

8.0 MB

Part 06-Module 01-Lesson 04_Functions/11. L4 08 Lambda Expressions V3-wkEmPz1peJM.mp4

8.0 MB

Part 02-Module 01-Lesson 04_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4

8.0 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/01. Welcome-SaSzn718doY.mp4

8.0 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/38. Bloopers Intro 1 V1-Y1weHponR2Q.mp4

8.0 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4

8.0 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/10. How The Gaussian Class Works-N-5I0d1zJHI.mp4

8.1 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4

8.1 MB

Part 06-Module 01-Lesson 07_Pandas/12. Pandas 7 V1-ruTYp-twXO0.mp4

8.1 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/29. Outliers How To Find Them-ksqzOCSAp5U.mp4

8.1 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/10. 03 Optimizing Common Books V1-WF9n_19V08g.mp4

8.1 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V1-BafXxtTuZgQ.mp4

8.1 MB

Part 10-Module 01-Lesson 09_Staying In Sync With A Remote Repository/05. L3 - Squashing In Action-cL6ehKtJLUM.mp4

8.2 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/04. 01 Writing Clean Code V1-wNaiahWCwkQ.mp4

8.2 MB

Part 12-Module 01-Lesson 06_Conditional Probability/14. Two Coins 4-9R44IyZ-aQI.mp4

8.2 MB

Part 08-Module 01-Lesson 01_Data Visualization in Data Analysis/04. Exploratory vs. Explanatory Analysis-wvgBSMks4p8.mp4

8.2 MB

Part 12-Module 01-Lesson 11_Confidence Intervals/08. Statistical vs. Practical Differences-RKHD1wzxxPA.mp4

8.3 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.mp4

8.3 MB

Part 11-Module 01-Lesson 03_Linear Combination/01. Linear Combinations 1-fmal7UE7dEE.mp4

8.3 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/20. Interactions And Higher Order Terms-AOfXMiJgo48.mp4

8.3 MB

Part 10-Module 01-Lesson 07_Working With Remotes/01. Intro-SBUOhyXcR1Q.mp4

8.3 MB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/01. 01 Welcome V1 V2-Ykd7CN5dDx0.mp4

8.4 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/18. Recommendations 1 17a 4422330 V1-DJfwhP_vvh4.mp4

8.4 MB

Part 06-Module 01-Lesson 01_Why Python Programming/04. L1 02 Course Overview V4-vFxXSIV5cHM.mp4

8.4 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.mp4

8.4 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4

8.4 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/09. L2 06 Efficient Code V1 V2-LbtxY7xetBw.mp4

8.4 MB

Part 14-Module 01-Lesson 01_The Data Science Process/33. Imputing Missing Values-CEWIPjz_gCE.mp4

8.4 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 4381128 V1-B6bELCg6gMs.mp4

8.5 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/14. Using Color-6bAedqD3ilw.mp4

8.5 MB

Part 02-Module 01-Lesson 05_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4

8.5 MB

Part 14-Module 01-Lesson 01_The Data Science Process/11. How To Break Into The Field-0-Y39LZ80VE.mp4

8.5 MB

Part 06-Module 01-Lesson 06_NumPy/07. NumPy 3 V1-Rt4aydeo9F8.mp4

8.5 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/23. Connecting Errors and P-Values-hFNjd5l9CLs.mp4

8.6 MB

Part 11-Module 01-Lesson 01_Introduction/03. Essence Of Linear Algebra Intro -EHcxDZpeGFg.mp4

8.6 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/22. Recommendations 1 20 10491855 V2-pjoxB00grHw.mp4

8.6 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 20332637 V1-UnDocJ9VUec.mp4

8.7 MB

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/01. C4 Intro-gXlqR86h0yI.mp4

8.7 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4

8.7 MB

Part 02-Module 01-Lesson 02_Linear Regression/26. Regularization-PyFNIcsNma0.mp4

8.8 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 012725 V1-Y1dN-mB39rM.mp4

8.8 MB

Part 06-Module 01-Lesson 04_Functions/01. Introduction-p5L4rTV1Pgk.mp4

8.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 0950 V1-yrNZ0sQwNcs.mp4

8.9 MB

Part 10-Module 01-Lesson 01_What is Version Control/01. Gitfinal L1 01 Welcome-lbR82UD5F0c.mp4

8.9 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/18. Joining Subqueries-rxy-fE5GeLY.mp4

8.9 MB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Pitching to a Recruiter-LxAdWaA-qTQ.mp4

8.9 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/19. Recommendations 1 17b 36044330 V1-b5gFe8Ij-g0.mp4

9.0 MB

Part 04-Module 01-Lesson 01_Clustering/09. 10 KMeans In Scikit Learn V1-jkEgQLOcCGo.mp4

9.0 MB

Part 06-Module 01-Lesson 04_Functions/05. Variable Scope-rYubQlAM-gw.mp4

9.0 MB

Part 12-Module 01-Lesson 04_Probability/15. One Of Three 1-bDCXSxkochE.mp4

9.0 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/01. Welcome To DSND T2 V1 1 V1-ebJZrc2y85Q.mp4

9.1 MB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/02. Roles Of A Data Engineer-f57UbUlSDgo.mp4

9.1 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples Applications MAIN V1 V2-HTahFoQwk2g.mp4

9.2 MB

Part 04-Module 01-Lesson 04_PCA/15. 14 Interpretation Solution V1-wU2duZa0ds0.mp4

9.2 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4

9.2 MB

Part 02-Module 01-Lesson 07_Ensemble Methods/03. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4

9.2 MB

Part 04-Module 01-Lesson 05_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4

9.2 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/15. Recommendations 1 14 10131720 V1-DWHYK0XSI70.mp4

9.2 MB

Part 02-Module 01-Lesson 09_Training and Tuning/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4

9.2 MB

Part 02-Module 01-Lesson 04_Decision Trees/14. Information Gain-k9iZL53PAmw.mp4

9.2 MB

Part 07-Module 01-Lesson 03_SQL Aggregations/01. Introduction to Aggregations-5vRf_Ntoxfw.mp4

9.2 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/07. 10 Flips 5 Heads-mOPFQlKBg2M.mp4

9.2 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/03. World Bank Datasets-lNPzOLzZVbw.mp4

9.3 MB

Part 12-Module 01-Lesson 07_Bayes Rule/13. Normalizing Probability-yYqN9Mf4jqw.mp4

9.3 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4

9.3 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/05. Richard Sharp Data Science-r0BCM6vhl0Q.mp4

9.3 MB

Part 14-Module 01-Lesson 01_The Data Science Process/05. Using Workspaces-45N9NK6kQ0Y.mp4

9.3 MB

Part 02-Module 01-Lesson 05_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4

9.3 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/16. Type Type Conversion-yN6Fam_vZrU.mp4

9.4 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/02. Introduction-Vnj2VNQROtI.mp4

9.6 MB

Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/04. L5 Outro-rW1YP1aSb08.mp4

9.6 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/26. Scikitlearn Source Code-4_qkqMsbthg.mp4

9.6 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.mp4

9.7 MB

Part 15-Module 01-Lesson 06_Web Development/12. 14 Screencast JavaScript V2-vgXUKgsT_48.mp4

9.7 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4

9.7 MB

Part 12-Module 01-Lesson 16_Logistic Regression/04. Fitting Logistic Regression In Python-baQf-XiZQQ4.mp4

9.7 MB

Part 12-Module 01-Lesson 16_Logistic Regression/02. Fitting Logistic Regression-Dg0rBDQnIYg.mp4

9.7 MB

Part 08-Module 01-Lesson 02_Design of Visualizations/10. Data Ink Ratio-gW2FapuYV4A.mp4

9.8 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/09. SVD-t2XTuHq6-xc.mp4

9.8 MB

Part 06-Module 01-Lesson 06_NumPy/08. NumPy 4 V1-jeU7lLgyMms.mp4

9.8 MB

Part 15-Module 01-Lesson 02_Software Engineering Practices Pt I/02. L2 02 Clean Mod Code Vid 1 V1 V2-RjHV8kRpVbA.mp4

9.8 MB

Part 04-Module 01-Lesson 01_Clustering/11. 12 KMeans In Scikit Learn Solution V1-IIVsWFq2DXk.mp4

9.8 MB

Part 12-Module 01-Lesson 07_Bayes Rule/24. Robot Sensing 4-vasdN2Gol0M.mp4

9.9 MB

Part 04-Module 01-Lesson 05_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4

9.9 MB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/04. Elevator Pitch-0QtgTG49E9I.mp4

10 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/13. Dummy Variables Recap-r7Lek8rsIcg.mp4

10 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/01. Introduction to Multiple Linear Regression-b26v8HK-8-o.mp4

10 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/10. Meet the Careers Team-cuKecPpZ7PM.mp4

10 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/07. Meet the Careers Team-cuKecPpZ7PM.mp4

10 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. Assignment Operators-p_qfzL-x3Cs.mp4

10 MB

Part 02-Module 01-Lesson 10_Finding Donors Project/06. Kaggle Project Final For Classroom-Ssttix340C8.mp4

10 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Kaggle Project Final For Classroom-Ssttix340C8.mp4

10 MB

Part 14-Module 01-Lesson 01_The Data Science Process/14. Bootcamps-l2tYmee3kxo.mp4

10 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/16. Binomial Distribution Conclusion-9gjCYs8f_PU.mp4

10 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4

10 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4

10 MB

Part 15-Module 01-Lesson 06_Web Development/05. 6 Screencast HTML Code V2-G7fBus1JSc0.mp4

10 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 14502145 V1-cvQngTUOWbM.mp4

10 MB

Part 10-Module 01-Lesson 01_What is Version Control/03. Gitfinal L1 13 Git'S Terminology-bf26adzeqMM.mp4

10 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/23. Interpreting Interactions-XV6S2srsdxw.mp4

10 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/24. Recommendations 2 25 V1-zgz5WYlI5fE.mp4

10 MB

Part 06-Module 01-Lesson 05_Scripting/21. The Standard Library-Fw3vf0tDrJM.mp4

10 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/11. Recommendations 2 10 4321430 V1-zVGhBQNgbc4.mp4

11 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/14. 14 Funk SVD-H8gdwXy_npI.mp4

11 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/02. Hypothesis Testing-9GbHHpiK6wk.mp4

11 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/31. L2 02 Dictionaries And Identiy Operators V3-QR8HTxCTWi0.mp4

11 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/08. Formula-DTdS-LlMTQ0.mp4

11 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/09. SQL Subquery Video-10pmKmTI_CA.mp4

11 MB

Part 09-Module 01-Lesson 01_Shell Workshop/01. Shell Intro--EtN5oD8MM0.mp4

11 MB

Part 12-Module 01-Lesson 16_Logistic Regression/01. Introduction to Logistic Regression-P_f2RjjnPEg.mp4

11 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/14. Common Types of Hypothesis Tests-8hv8KnvQ6JY.mp4

11 MB

Part 06-Module 01-Lesson 05_Scripting/17. Reading And Writing Files-w-ZG6DMkVi4.mp4

11 MB

Part 06-Module 01-Lesson 03_Control Flow/20. L3 08 While Loops V3-7Sf5tcPlKQw.mp4

11 MB

Part 12-Module 01-Lesson 06_Conditional Probability/12. Two Coins 2-hoVOT8qcQ7c.mp4

11 MB

Part 12-Module 01-Lesson 16_Logistic Regression/07. Interpreting Results in Python-IY88UTiJltQ.mp4

11 MB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/01. Introduction To Software Engineering-7kphieW4yl4.mp4

11 MB

Part 12-Module 01-Lesson 07_Bayes Rule/30. Sebastian At Home-R4zq6mPPMxs.mp4

11 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/02. Arithmetic Operators-M8TIOK2P2yw.mp4

11 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/02. Shape-DjsL64Kjr1Q.mp4

11 MB

Part 12-Module 01-Lesson 04_Probability/16. One Of Three 2-27Ed1GI4j84.mp4

11 MB

Part 03-Module 01-Lesson 02_Implementing Gradient Descent/03. Gradient Descent-Math-7sxA5Ap8AWM.mp4

11 MB

Part 12-Module 01-Lesson 04_Probability/18. Doubles-fkUyTJNbdzU.mp4

11 MB

Part 12-Module 01-Lesson 16_Logistic Regression/06. Interpreting Results-UPOxxbKu6CQ.mp4

11 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/18. Multicollinearity VIFs-uiF3UcDWwPI.mp4

11 MB

Part 12-Module 01-Lesson 04_Probability/02. Flipping Coins-OpNufHYgJCg.mp4

11 MB

Part 06-Module 01-Lesson 05_Scripting/20. Importing Files-qjeSn6zZbR0.mp4

11 MB

Part 12-Module 01-Lesson 07_Bayes Rule/21. Robot Sensing 1-_DjfTytro6I.mp4

11 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/09. Types Of Errors - Part II-mbdSQ5CjdFs.mp4

12 MB

Part 12-Module 01-Lesson 06_Conditional Probability/10. Total Probability-YSYpzFR4k1I.mp4

12 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/03. BMG Inspiration-ulMqa4YWbvc.mp4

12 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4

12 MB

Part 07-Module 01-Lesson 07_[Advanced] SQL Advanced JOINs Performance Tuning/15. More on Performance Tuning-ZK1FvNH10Ag.mp4

12 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/06. 5 Flips 3 Heads-pOKmt4w8T3g.mp4

12 MB

Part 12-Module 01-Lesson 12_Hypothesis Testing/27. What If Our Sample Is Large-WoTCeSTL1eM.mp4

12 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4

12 MB

Part 14-Module 01-Lesson 01_The Data Science Process/23. What Happened-gLn6_Z3nwcc.mp4

12 MB

Part 10-Module 01-Lesson 03_Review a Repo's History/01. A Repository's History - Intro-UBmg3syQS0E.mp4

12 MB

Part 02-Module 01-Lesson 04_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4

12 MB

Part 12-Module 01-Lesson 07_Bayes Rule/08. Bayes Rule Diagram-b8M9CWxRyQ4.mp4

12 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/13. Formula Summary-zqo1RJEHT_0.mp4

13 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4

13 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 33514421 V1-TCaeEdrbYRc.mp4

13 MB

Part 02-Module 01-Lesson 04_Decision Trees/07. Entropy-piLpj1V1HEk.mp4

13 MB

Part 16-Module 01-Lesson 01_Introduction to Data Engineering/03. Figure 8 Project V2-adtlHL42AuQ.mp4

13 MB

Part 06-Module 01-Lesson 03_Control Flow/07. Truth Value Testing-e52uw7ejV8k.mp4

13 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 18003113 V1-2M-WX2X2ts4.mp4

13 MB

Part 04-Module 01-Lesson 04_PCA/10. 09 PCA V1-0RLDZWeq5JE.mp4

13 MB

Part 12-Module 01-Lesson 06_Conditional Probability/13. Two Coins 3-JIWv5fU3GLA.mp4

13 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4

13 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4

13 MB

Part 05-Module 01-Lesson 01_Congratulations!/03. Next Steps-kXMCKZ4HqsM.mp4

13 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/19. Recommendations 2 18 11442204 V1-8kdRNQnqSGA.mp4

13 MB

Part 02-Module 01-Lesson 04_Decision Trees/15. Maximizing Information Gain-3FgJOpKfdY8.mp4

13 MB

Part 14-Module 01-Lesson 01_The Data Science Process/37. Imputing Values-nTM4HiDneeE.mp4

13 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/10. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4

13 MB

Part 06-Module 01-Lesson 03_Control Flow/25. Break and Continue-F6qJAv9ts9Y.mp4

13 MB

Part 05-Module 01-Lesson 01_Congratulations!/01. Congrats!-P3MfbMs-D98.mp4

13 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/01. Introduction-4F7SC0C6tfQ.mp4

13 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4

13 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/25. L2 06 Lists Methods V1-tz2Ja1Eaeqo.mp4

13 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/11. Dummy Variable Interpretation-TxP_TD0kbOo.mp4

13 MB

Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/10. Linear Transformations 2-imtEd8M6__s.mp4

14 MB

Part 17-Module 04-Lesson 01_Recommendation Engines/01. IBM Project Overview-XP_f64c07Gc.mp4

14 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4

14 MB

Part 05-Module 01-Lesson 01_Congratulations!/04. Build A Recommendation Engine IBM-A0rVwTbntf4.mp4

14 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Figure 8 Project-QbLVh5GTuJQ.mp4

14 MB

Part 16-Module 02-Lesson 01_Project Disaster Response Pipeline/01. Figure 8 Project-QbLVh5GTuJQ.mp4

14 MB

Part 05-Module 01-Lesson 01_Congratulations!/04. Figure 8 Project-QbLVh5GTuJQ.mp4

14 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/02. Multiple Linear Regression-rvYZp99nj6c.mp4

14 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/03. Part 1 V2-n4mbZYIfKb4.mp4

14 MB

Part 11-Module 01-Lesson 03_Linear Combination/02. Linear Combinations 2-RsKJNDTb8nw.mp4

14 MB

Part 06-Module 01-Lesson 06_NumPy/05. NumPy 2 V1-KR3hHf9Zxxg.mp4

14 MB

Part 02-Module 01-Lesson 05_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4

14 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/07. Meet The Instructors-ndyjFUF2e9Q.mp4

14 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/15. Potential Problems-lGwB6YRThbI.mp4

14 MB

Part 12-Module 01-Lesson 16_Logistic Regression/15. Confusion Matrix for Eigenfaces--VxKwVvrNY0.mp4

15 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/09. PyTorch - Part 7-hFu7GTfRWks.mp4

15 MB

Part 15-Module 01-Lesson 06_Web Development/16. 18 Screencast Plotly V2-QsmOW1jNeio.mp4

15 MB

Part 01-Module 04-Lesson 01_What Is Ahead/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4

15 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/01. What Do Data Scientists Do-sN2DbIJUZmw.mp4

15 MB

Part 12-Module 01-Lesson 07_Bayes Rule/03. Prior And Posterior-o2Tpws5C2Eg.mp4

15 MB

Part 06-Module 01-Lesson 03_Control Flow/07. Complex Boolean Expressions-gWmIKWgzFqI.mp4

15 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. Variables-7pxpUot4x0w.mp4

15 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/08. Números inteiros e floats-MiJ1vfWp-Ts.mp4

15 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4

16 MB

Part 06-Module 01-Lesson 04_Functions/02. Defining Functions-IP_tJYhynbc.mp4

16 MB

Part 14-Module 01-Lesson 01_The Data Science Process/17. Job Satisfaction-OjCNMhWlYh8.mp4

16 MB

Part 06-Module 01-Lesson 03_Control Flow/28. Zip and Enumerate-bSJPzVArE7M.mp4

16 MB

Part 12-Module 01-Lesson 06_Conditional Probability/15. Summary-yepMH9VswI8.mp4

16 MB

Part 15-Module 01-Lesson 06_Web Development/10. CSS-s_sdzHR9cs0.mp4

16 MB

Part 06-Module 01-Lesson 04_Functions/08. Documentation-_Vl9NJkA6JQ.mp4

16 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/08. Py Part 6 V1-HiTih59dCWQ.mp4

16 MB

Part 12-Module 01-Lesson 05_Binomial Distribution/09. Arrangements-GeINbOOYkF8.mp4

16 MB

Part 14-Module 01-Lesson 01_The Data Science Process/43. Putting It All Together-3SX4dMZPNEI.mp4

16 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/08. Dummy Variables--QTgDd-fZuA.mp4

16 MB

Part 17-Module 02-Lesson 04_Portfolio Exercise Starbucks/01. Starbucks Lab-QPKRboscAf4.mp4

16 MB

Part 15-Module 01-Lesson 04_Introduction to Object-Oriented Programming/27. 20 Putting Code On PyPi V1-4uosDOKn5LI.mp4

16 MB

Part 14-Module 01-Lesson 01_The Data Science Process/07. A Look at the Data-vPHVUYvCNGE.mp4

16 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/02. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4

16 MB

Part 04-Module 01-Lesson 06_Project Identify Customer Segments/01. Creating Customer Segmentation Arvato Project-VCChvqoK6Go.mp4

16 MB

Part 15-Module 01-Lesson 01_Introduction to Software Engineering/04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.mp4

17 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/05. L2 04b Variables II V3-4IJqbP8vi6A.mp4

17 MB

Part 12-Module 01-Lesson 07_Bayes Rule/20. Bayes Rule Summary-RgXQ8GRsjfc.mp4

17 MB

Part 06-Module 01-Lesson 03_Control Flow/02. If Statements-jWiIUMrwPqA.mp4

17 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/22. Recommendations 2 21a 01725 V1-UFmfDAiaOmw.mp4

17 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4

17 MB

Part 15-Module 01-Lesson 06_Web Development/27. 40 Screencast Flask Pandas Plotly Part4 V2-4IF2G9Fehb4.mp4

17 MB

Part 01-Module 04-Lesson 01_What Is Ahead/04. What'S Ahead Figure 8 Fix-SE4TQnOwmBI.mp4

17 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/13. Strings-ySZDrs-nNqg.mp4

17 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/06. Interpreting Multiplie Linear Regression Coefficients-qRD3OVX8UMM.mp4

17 MB

Part 07-Module 01-Lesson 04_SQL Subqueries Temporary Tables/11. Subquery Solution Video-Y6S3S0LsMrw.mp4

17 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/22. Interactions Higher Order Terms-gMHwogzqPOk.mp4

17 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/02. Meet The Instructors-XAU2Nf51vfU.mp4

17 MB

Part 14-Module 01-Lesson 01_The Data Science Process/21. Predicting Salary-HTp4LA1MJh8.mp4

17 MB

Part 06-Module 01-Lesson 03_Control Flow/31. List Comprehensions-6qxo-NV9v_s.mp4

17 MB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/01. Why Network-exjEm9Paszk.mp4

17 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/17. Multicollinearity VIFs-wbtrXMusDe8.mp4

18 MB

Part 14-Module 01-Lesson 01_The Data Science Process/32. Removing Data Part II-lPl6-Z098Rs.mp4

18 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4

18 MB

Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/08. Advanced API Code Walk-through-AkqO534YooE.mp4

18 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples Applications MAIN V1 V2-GhyFsjQ4FkA.mp4

18 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/06. Recommendations 1 6 11123244 V1-QlILlYuWF9U.mp4

18 MB

Part 12-Module 01-Lesson 07_Bayes Rule/02. Cancer Test-CNpSrdnYvbo.mp4

18 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4

18 MB

Part 20-Module 01-Lesson 03_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4

18 MB

Part 06-Module 01-Lesson 03_Control Flow/02. If Elif and Else-KZubH5XT0eU.mp4

18 MB

Part 14-Module 01-Lesson 01_The Data Science Process/35. Imputation Methods-OwEWSBitF-Q.mp4

18 MB

Part 12-Module 01-Lesson 15_Multiple Linear Regression/05. How Does MLR Work-bvM6eUYyurA.mp4

18 MB

Part 06-Module 01-Lesson 03_Control Flow/10. For Loops-UtX0PXSUCdY.mp4

18 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4

19 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4

19 MB

Part 01-Module 04-Lesson 01_What Is Ahead/02. Adam from IBM-NjjtY5UHyac.mp4

19 MB

Part 17-Module 03-Lesson 02_Matrix Factorization for Recommendations/16. Recommendations 2 16 1051320 V1-_4N6h82szWo.mp4

19 MB

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/04. Experimental Design Insights With Richard Sharp-XDBw2nfOrsU.mp4

19 MB

Part 06-Module 01-Lesson 04_Functions/14. Iterators And Generators-tYH8X4Zeh-0.mp4

19 MB

Part 15-Module 01-Lesson 06_Web Development/30. Deployment-YPfNzpnm_Rk.mp4

19 MB

Part 16-Module 01-Lesson 03_NLP Pipelines/04. Cleaning-qawXp9DPV6I.mp4

20 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/11. MLND - Unsupervised Learning - L3 11 Visual Example Of EM Progress MAIN V1 V1-9x3d_eVJrJE.mp4

20 MB

Part 06-Module 01-Lesson 03_Control Flow/07. Good And Bad Examples-95oLh3WtdhY.mp4

20 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4

20 MB

Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/11. Linear Transformations 3-g_yTyRwMzXU.mp4

20 MB

Part 17-Module 03-Lesson 01_Introduction to Recommendation Engines/09. Recommendations 1 9 03362 V1-MwRSg5RASoc.mp4

20 MB

Part 04-Module 01-Lesson 04_PCA/12. 11 PCA 1 Solution V1-u0rJRmubQ44.mp4

20 MB

Part 15-Module 01-Lesson 07_Portfolio Exercise Deploy a Data Dashboard/06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.mp4

20 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/33. Data Engineering Importance-VO-OrJ0JqxM.mp4

21 MB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/03. Elevator Pitch-S-nAHPrkQrQ.mp4

21 MB

Part 11-Module 01-Lesson 04_Linear Transformation and Matrices/09. Linear Transformations 1-99jYIxBRDww.mp4

21 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/08. Whitespace-UxkIwcOczQQ.mp4

21 MB

Part 02-Module 01-Lesson 05_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4

21 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/10. Boolean Comparison and Logical Operators-iNNsUJIDtVU.mp4

22 MB

Part 02-Module 01-Lesson 04_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4

22 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/05. Interview with Art - Part 1-ClLYamtaO-Q.mp4

22 MB

Part 14-Module 01-Lesson 01_The Data Science Process/40. Categorical Variables-p3gDUkBD9uM.mp4

22 MB

Part 12-Module 01-Lesson 09_Normal Distribution Theory/01. Maximum Probability-b2zvrFL8AUw.mp4

22 MB

Part 12-Module 01-Lesson 06_Conditional Probability/03. Medical Example 2-FV_hc3MzS_8.mp4

22 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4

22 MB

Part 12-Module 01-Lesson 16_Logistic Regression/29. Model Diagnostics In Python-1Z4eorbfOOc.mp4

23 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.mp4

23 MB

Part 04-Module 01-Lesson 02_Hierarchical and Density Based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4

23 MB

Part 06-Module 01-Lesson 02_Data Types and Operators/19. String Methods-Bv7CAxVOONs.mp4

24 MB

Part 14-Module 01-Lesson 01_The Data Science Process/13. How to Break Into the Field Solution-Db_2Lmwo4EY.mp4

24 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/10. Py Part 8 V1-3eqn5sgCOsY.mp4

25 MB

Part 14-Module 02-Lesson 01_Optimize Your GitHub Profile/14. Interview with Art - Part 3-M6PKr3S1rPg.mp4

25 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4

25 MB

Part 05-Module 01-Lesson 01_Congratulations!/04. Introduction to Blogging for Data Science-WrvGpRN5XQI.mp4

25 MB

Part 14-Module 01-Lesson 03_Project Write A Data Science Blog Post/01. Blogging for Data Science-WrvGpRN5XQI.mp4

25 MB

Part 05-Module 01-Lesson 01_Congratulations!/04. Arvato Final Project-qBR6A0IQXEE.mp4

25 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/03. Arvato Final Project-qBR6A0IQXEE.mp4

25 MB

Part 18-Module 01-Lesson 01_Data Scientist Capstone/06. Arvato Final Project-qBR6A0IQXEE.mp4

25 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/04. DSND T2 Intro Dan Frank V4-rTCPmVQDsEw.mp4

26 MB

Part 17-Module 01-Lesson 01_Intro to Experiment Design and Recommendation Engines/03. Dan Frank Interview-Me-KRvZW1QQ.mp4

26 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/10. MLND - Unsupervised Learning - L3 10 Expectation Maximization Pt 2 MAIN V1 V2-B_xXd0mFUm4.mp4

26 MB

Part 15-Module 01-Lesson 06_Web Development/14. Bootstrap Library-KsrqjguHWUI.mp4

26 MB

Part 01-Module 04-Lesson 01_What Is Ahead/03. Rachel from Kaggle-uVsYYzxbyIg.mp4

26 MB

Part 02-Module 01-Lesson 06_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4

27 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/07. Py Part 5 V2-coBbbrGZXI0.mp4

27 MB

Part 13-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree Program/06. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4

27 MB

Part 01-Module 01-Lesson 01_Welcome to the Data Scientist Nanodegree program/08. L1 06 How To Succeed REPLACEMENT-JRnZOZR97QQ.mp4

27 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/05. Py Part 3 V2-u8hDj5aJK6I.mp4

28 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4

32 MB

Part 16-Module 03-Lesson 01_Strengthen Your Online Presence Using LinkedIn/02. Meet Chris-0ccflD9x5WU.mp4

32 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/09. MLND - Unsupervised Learning - L3 09 Expectation Maximization Pt 1 V1 MAIN 1 V2-cf-RLKn5ubA.mp4

33 MB

Part 03-Module 01-Lesson 05_Deep Learning with PyTorch/04. Py Part 2 V1-u50_ZyKqt8g.mp4

35 MB

Part 16-Module 01-Lesson 02_ETL Pipelines/24. Data Engineering-z6r2e_V0Td0.mp4

35 MB

Part 13-Module 01-Lesson 04_The Skills That Set You Apart/02. What Do Data Scientists at AirBnB Do-q7sw9vc5o1U.mp4

40 MB

Part 04-Module 01-Lesson 03_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4

41 MB