Udacity - Machine Learning Engineer Nanodegree nd009t v1.0.0

File Name

Size

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.en.vtt

104 B

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.pt-BR.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.zh-CN.vtt

107 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.en.vtt

108 B

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.pt-BR.vtt

109 B

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.en.vtt

109 B

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.zh-CN.vtt

113 B

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-p5p3OLARpmA.ar.vtt

118 B

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-FZVBF1HR4U0.ar.vtt

122 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.pt-BR.vtt

124 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.zh-CN.vtt

125 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.zh-CN.vtt

125 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.en.vtt

138 B

Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.en.vtt

139 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-vmIK4jpUtNo.ar.vtt

140 B

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.en.vtt

140 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.en.vtt

141 B

Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.pt-BR.vtt

141 B

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.pt-BR.vtt

143 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.pt-BR.vtt

164 B

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.en.vtt

164 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.zh-CN.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.zh-CN.vtt

166 B

Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.zh-CN.vtt

166 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.zh-CN.vtt

167 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.ar.vtt

168 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron--dT9dztM-Lc.ar.vtt

171 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality--dcNhrSPmoY.pt-BR.vtt

171 B

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.pt-BR.vtt

180 B

Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.pt-BR.vtt

186 B

Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-th34aboBOO0.ar.vtt

203 B

Part 04-Module 03-Lesson 01_Feature Scaling/04. Sarah's Height + Weight-OdsfV143AMc.ar.vtt

204 B

Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.pt-BR.vtt

204 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.en.vtt

205 B

Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.zh-CN.vtt

206 B

Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.en.vtt

207 B

Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.en.vtt

208 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.en.vtt

214 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.zh-CN.vtt

222 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.pt-BR.vtt

226 B

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.ar.vtt

226 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.en.vtt

229 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.pt-BR.vtt

230 B

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.zh-CN.vtt

232 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.pt-BR.vtt

233 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.zh-CN.vtt

243 B

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.zh-CN.vtt

245 B

Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie-knrPsGtpyQY.ar.vtt

258 B

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.pt-BR.vtt

271 B

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.en.vtt

273 B

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.zh-CN.vtt

277 B

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.zh-CN.vtt

277 B

Part 04-Module 03-Lesson 01_Feature Scaling/03. Height + Weight for Cameron-MetxO9LDp-I.ar.vtt

282 B

Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-sPqs7DoBkXQ.ar.vtt

284 B

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.pt-BR.vtt

292 B

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.en.vtt

292 B

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.en.vtt

298 B

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.zh-CN.vtt

299 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.ar.vtt

301 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.zh-CN.vtt

301 B

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.pt-BR.vtt

302 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en.vtt

303 B

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.zh-CN.vtt

305 B

Part 04-Module 03-Lesson 01_Feature Scaling/08. Feature Scaling Formula Quiz 2-J6RyUyWxrM4.ar.vtt

306 B

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.pt-BR.vtt

306 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.en-US.vtt

309 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.en.vtt

312 B

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.en.vtt

315 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.zh-CN.vtt

316 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.en.vtt

320 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.pt-BR.vtt

324 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.en.vtt

325 B

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.en.vtt

325 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.pt-BR.vtt

326 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.pt-BR.vtt

326 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.pt-BR.vtt

331 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.zh-CN.vtt

332 B

Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.zh-CN.vtt

335 B

Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.zh-CN.vtt

342 B

Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.zh-CN.vtt

355 B

Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.en.vtt

355 B

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-bAZJT4xHiXM.ar.vtt

357 B

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.zh-CN.vtt

357 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.ar.vtt

359 B

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-QsncWsyboFk.ar.vtt

360 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.zh-CN.vtt

361 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.zh-CN.vtt

361 B

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.pt-BR.vtt

362 B

Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt

364 B

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

364 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.en.vtt

368 B

Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.pt-BR.vtt

369 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.zh-CN.vtt

369 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.pt-BR.vtt

370 B

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.en.vtt

371 B

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.zh-CN.vtt

385 B

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-nvLhUSSUhiY.ar.vtt

385 B

Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt

390 B

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

390 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.zh-CN.vtt

392 B

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.ar.vtt

393 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.en.vtt

395 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.zh-CN.vtt

396 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.en.vtt

399 B

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.pt-BR.vtt

402 B

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.en.vtt

406 B

Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.zh-CN.vtt

408 B

Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.zh-CN.vtt

410 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.pt-BR.vtt

410 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.en.vtt

418 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.en.vtt

419 B

Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.en.vtt

419 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.zh-CN.vtt

419 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.pt-BR.vtt

420 B

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

420 B

Part 05-Module 01-Lesson 01_Neural Networks/10. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt

420 B

Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.pt-BR.vtt

421 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.zh-CN.vtt

422 B

Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.pt-BR.vtt

423 B

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.zh-CN.vtt

424 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-g5yfjKWIKN4.ar.vtt

425 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.zh-CN.vtt

425 B

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-vIxDt0bNV9g.ar.vtt

425 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.pt-BR.vtt

426 B

Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.zh-CN.vtt

432 B

Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.en.vtt

435 B

Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.zh-CN.vtt

437 B

Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.zh-CN.vtt

438 B

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.pt-BR.vtt

439 B

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.zh-CN.vtt

440 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.ar.vtt

444 B

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.en.vtt

451 B

Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.pt-BR.vtt

453 B

README.txt

454 B

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.pt-BR.vtt

454 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.pt-BR.vtt

454 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.zh-CN.vtt

456 B

Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.en.vtt

457 B

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.pt-BR.vtt

457 B

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.en.vtt

458 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.pt-BR.vtt

460 B

Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.pt-BR.vtt

465 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.en.vtt

466 B

Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.zh-CN.vtt

467 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.zh-CN.vtt

468 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.pt-BR.vtt

472 B

Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.en.vtt

472 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.en.vtt

473 B

Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.zh-CN.vtt

473 B

Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.pt-BR.vtt

474 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.zh-CN.vtt

475 B

Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.en.vtt

476 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.ja-JP.vtt

477 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/20. Random Restart-idyBBCzXiqg.pt-BR.vtt

478 B

Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.ar.vtt

479 B

Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt

481 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.pt-BR.vtt

482 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.en.vtt

483 B

Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.zh-CN.vtt

485 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.zh-CN.vtt

487 B

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.zh-CN.vtt

488 B

Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.zh-CN.vtt

488 B

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.en.vtt

489 B

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.ar.vtt

490 B

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.ar.vtt

490 B

Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.en.vtt

495 B

Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.pt-BR.vtt

497 B

Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.zh-CN.vtt

498 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.zh-CN.vtt

499 B

Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.en.vtt

501 B

Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt

501 B

Part 04-Module 04-Lesson 01_PCA/02. Trickier Data Dimensionality-s24-ikl3ZAs.ar.vtt

505 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.en.vtt

505 B

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.zh-CN.vtt

507 B

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.pt-BR.vtt

507 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.en.vtt

508 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.en.vtt

510 B

Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-9J3IwQFXveI.ar.vtt

510 B

Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.ar.vtt

512 B

Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.pt-BR.vtt

512 B

Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.en.vtt

514 B

Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.en.vtt

514 B

Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.en.vtt

517 B

Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.pt-BR.vtt

518 B

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.ar.vtt

521 B

Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.zh-CN.vtt

524 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/07. 07 Quiz Data Challenges V1-F8yc7BlV93c.en.vtt

526 B

Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.zh-CN.vtt

530 B

Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.pt-BR.vtt

533 B

Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.zh-CN.vtt

535 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.pt-BR.vtt

538 B

Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.pt-BR.vtt

538 B

Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.pt-BR.vtt

540 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.zh-CN.vtt

540 B

Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.en.vtt

540 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.ar.vtt

542 B

Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.pt-BR.vtt

543 B

Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt

545 B

Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.pt-BR.vtt

549 B

Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.en.vtt

551 B

Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.pt-BR.vtt

551 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.zh-CN.vtt

555 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.zh-CN.vtt

556 B

Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.zh-CN.vtt

557 B

Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.en.vtt

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Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.ar.vtt

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Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.zh-CN.vtt

560 B

Part 04-Module 02-Lesson 04_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 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-e83ZS4VqGZ0.ar.vtt

561 B

Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.zh-CN.vtt

568 B

Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.ar.vtt

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.pt-BR.vtt

573 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.en.vtt

573 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.pt-BR.vtt

574 B

Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.en.vtt

579 B

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.ar.vtt

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Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.en.vtt

586 B

Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.zh-CN.vtt

588 B

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.zh-CN.vtt

589 B

Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.zh-CN.vtt

590 B

Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.pt-BR.vtt

590 B

Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.pt-BR.vtt

592 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.zh-CN.vtt

593 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.en.vtt

594 B

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.en.vtt

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Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.en.vtt

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Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.ar.vtt

597 B

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.pt-BR.vtt

599 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt

600 B

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.en.vtt

600 B

Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.en.vtt

601 B

Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.en.vtt

601 B

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.pt-BR.vtt

606 B

Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.en.vtt

607 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.ar.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.en-US.vtt

608 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.zh-CN.vtt

612 B

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.en.vtt

613 B

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt

615 B

Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.en.vtt

622 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-xTEkF0voyoM.ar.vtt

624 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt

624 B

Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.zh-CN.vtt

631 B

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.zh-CN.vtt

633 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.en.vtt

633 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.en.vtt

634 B

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.en.vtt

635 B

Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.pt-BR.vtt

638 B

Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.en-US.vtt

638 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.pt-BR.vtt

643 B

Part 11-Module 04-Lesson 01_Deep Neural Networks/10. Regularization-Quiz-E0eEW6V0_sA.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.zh-CN.vtt

655 B

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.pt-BR.vtt

655 B

Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.pt.vtt

656 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.pt-BR.vtt

657 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.zh-CN.vtt

662 B

Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.zh-CN.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.pt-BR.vtt

663 B

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.en.vtt

665 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.zh-CN.vtt

668 B

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.pt-BR.vtt

671 B

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.pt-BR.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.zh-CN.vtt

675 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.zh-CN.vtt

677 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.pt-BR.vtt

678 B

Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt

678 B

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.zh-CN.vtt

680 B

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.ar.vtt

682 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.pt-BR.vtt

683 B

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.pt-BR.vtt

683 B

Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.en.vtt

685 B

Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en.vtt

688 B

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.pt-BR.vtt

688 B

Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.pt-BR.vtt

690 B

Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.zh-CN.vtt

692 B

Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.ar.vtt

694 B

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.en.vtt

694 B

Part 04-Module 02-Lesson 04_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 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.ar.vtt

697 B

Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.en.vtt

701 B

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.zh-CN.vtt

701 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.en.vtt

702 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.pt-BR.vtt

705 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.en.vtt

707 B

Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.pt-BR.vtt

707 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.pt-BR.vtt

707 B

Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.es-MX.vtt

707 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.zh-CN.vtt

709 B

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-cTjBlM2ATLQ.ar.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.en.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.en-US.vtt

716 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.pt-BR.vtt

716 B

Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.zh-CN.vtt

718 B

Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt

719 B

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.zh-CN.vtt

720 B

Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.pt-BR.vtt

723 B

Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.zh-CN.vtt

723 B

Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.en.vtt

725 B

Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.zh-CN.vtt

727 B

Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.pt-BR.vtt

727 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.zh-CN.vtt

729 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.pt-BR.vtt

730 B

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.zh-CN.vtt

733 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.zh-CN.vtt

734 B

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.pt-BR.vtt

736 B

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.pt-BR.vtt

737 B

Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.zh-CN.vtt

739 B

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.en.vtt

739 B

Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-20QVVrTcp2A.ar.vtt

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Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.en.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.pt-BR.vtt

754 B

Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.zh-CN.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.pt-BR.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.en-US.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.zh-CN.vtt

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Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.ar.vtt

768 B

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.en.vtt

768 B

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.ar.vtt

769 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.pt-BR.vtt

769 B

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.zh-CN.vtt

769 B

Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.en-US.vtt

770 B

Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.en.vtt

771 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.en.vtt

775 B

Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.ar.vtt

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.pt-BR.vtt

786 B

Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.zh-CN.vtt

787 B

Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.en.vtt

791 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/19. 17 Quiz ROC Curve 1 PT2 V1-Xv3v59_CfEU.en.vtt

791 B

Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.pt-BR.vtt

793 B

Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.en-US.vtt

793 B

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.en.vtt

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Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.pt-BR.vtt

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Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.zh-CN.vtt

804 B

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.zh-CN.vtt

806 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.zh-CN.vtt

810 B

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.zh-CN.vtt

810 B

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.ar.vtt

812 B

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.zh-CN.vtt

812 B

Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt

813 B

Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.zh-CN.vtt

814 B

Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.pt-BR.vtt

817 B

Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.ar.vtt

820 B

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.en.vtt

820 B

Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.zh-CN.vtt

822 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.zh-CN.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/17. Numerical Stability-_SbGcOS-jcQ.pt-BR.vtt

823 B

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/20. Solution ROC Curve-sdUUf6RRmXI.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.en.vtt

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Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.pt-BR.vtt

826 B

Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.en.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.en.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.es-MX.vtt

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Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.ar.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.zh-CN.vtt

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.en-US.vtt

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Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.pt-BR.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.en.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/02. Confusion-Matrix-Solution-ywwSzyU9rYs.zh-CN.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.pt-BR.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.pt-BR.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.ar.vtt

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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.en.vtt

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Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.en.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.zh-CN.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.ar.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.zh-CN.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.zh-CN.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.zh-CN.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.pt-BR.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.pt-BR.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.pt-BR.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.ar.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/15. Local Minima-gF_sW_nY-xw.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.pt-BR.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.zh-CN.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.en.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/12. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.pt-BR.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.en-US.vtt

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/05. Moving A Line-8EIHFyL2Log.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.en.vtt

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.en.vtt

1.2 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.zh-CN.vtt

1.2 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.pt-BR.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.en.vtt

1.2 kB

Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.ar.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/img/e.gif

1.2 kB

Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.pt-BR.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.pt-BR.vtt

1.2 kB

Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.en-US.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.en.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.pt-BR.vtt

1.2 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.zh-CN.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.en.vtt

1.2 kB

Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.en.vtt

1.2 kB

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.en-US.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.zh-CN.vtt

1.2 kB

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.zh-CN.vtt

1.2 kB

Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.es-MX.vtt

1.2 kB

Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.pt-BR.vtt

1.2 kB

Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.zh-CN.vtt

1.2 kB

Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.pt-BR.vtt

1.2 kB

Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.ar.vtt

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Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.en.vtt

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Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.en.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt

1.2 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/img/linear-equation.gif

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Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.en.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/12. Dropout Pt. 2-8nG8zzJMbZw. 2 RENDER-8nG8zzJMbZw.en-US.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt

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Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.zh-CN.vtt

1.2 kB

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.en.vtt

1.2 kB

Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.pt-BR.vtt

1.2 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.ar.vtt

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Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.zh-CN.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/05. MLND SL EM 05 Weighting The Models MAIN V1-wn6K536dPLc.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.en-US.vtt

1.3 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.en.vtt

1.3 kB

Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt

1.3 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.zh-CN.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.en.vtt

1.3 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.en.vtt

1.3 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/02. MLND - Unsupervised Learning - L3 2 Gaussian Mixture Model Clustering MAIN V1 V2-Y_methsXoFA.pt-BR.vtt

1.3 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.zh-CN.vtt

1.3 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.zh-CN.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.pt-BR.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.zh-CN.vtt

1.3 kB

Part 03-Module 01-Lesson 01_Linear Regression/21. Polynomial Regression-DBhWG-PagEQ.en.vtt

1.3 kB

Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.en.vtt

1.3 kB

Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.zh-CN.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.pt-BR.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.en.vtt

1.3 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.pt-BR.vtt

1.3 kB

Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.pt-BR.vtt

1.3 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.zh-CN.vtt

1.3 kB

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt

1.3 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt

1.3 kB

Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-62.gif

1.3 kB

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.ar.vtt

1.3 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en.vtt

1.3 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.en-US.vtt

1.3 kB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.en.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.ar.vtt

1.3 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.zh-CN.vtt

1.3 kB

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.zh-CN.vtt

1.3 kB

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.ar.vtt

1.3 kB

Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.en.vtt

1.3 kB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.en.vtt

1.3 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.zh-CN.vtt

1.3 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/02. Continuous Perceptrons-07-JJ-aGEfM.en.vtt

1.3 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.pt-BR.vtt

1.3 kB

Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt

1.3 kB

Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.zh-CN.vtt

1.3 kB

Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.zh-CN.vtt

1.3 kB

Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.ar.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.pt-BR.vtt

1.4 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.pt-BR.vtt

1.4 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.pt-BR.vtt

1.4 kB

Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.en.vtt

1.4 kB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.zh-CN.vtt

1.4 kB

Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.en.vtt

1.4 kB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.zh-CN.vtt

1.4 kB

Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.en.vtt

1.4 kB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.pt-BR.vtt

1.4 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.en.vtt

1.4 kB

Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.pt-BR.vtt

1.4 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.zh-CN.vtt

1.4 kB

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.zh-CN.vtt

1.4 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.zh-CN.vtt

1.4 kB

Part 05-Module 01-Lesson 01_Neural Networks/09. Why Neural Networks-zAkzOZntK6Y.en.vtt

1.4 kB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.pt-BR.vtt

1.4 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/03. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt

1.4 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.zh-CN.vtt

1.4 kB

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.en.vtt

1.4 kB

Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.pt-BR.vtt

1.4 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.pt-BR.vtt

1.4 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.en.vtt

1.4 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.zh-CN.vtt

1.4 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.zh-CN.vtt

1.4 kB

Part 03-Module 01-Lesson 01_Linear Regression/img/y.gif

1.4 kB

Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.zh-CN.vtt

1.4 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.zh-CN.vtt

1.4 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.zh-CN.vtt

1.4 kB

Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.en.vtt

1.4 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.zh-CN.vtt

1.4 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.zh-CN.vtt

1.4 kB

Part 03-Module 01-Lesson 01_Linear Regression/04. Fitting A Line-gkdoknEEcaI.pt-BR.vtt

1.4 kB

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.en.vtt

1.4 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.en.vtt

1.4 kB

Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt

1.4 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.zh-CN.vtt

1.4 kB

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.en.vtt

1.4 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.zh-CN.vtt

1.4 kB

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.zh-CN.vtt

1.4 kB

Part 04-Module 02-Lesson 04_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 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.es-MX.vtt

1.4 kB

Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt

1.4 kB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.zh-CN.vtt

1.4 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en.vtt

1.4 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.en.vtt

1.4 kB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.en.vtt

1.4 kB

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.pt-BR.vtt

1.4 kB

Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.en-US.vtt

1.4 kB

Part 02-Module 01-Lesson 01_Training and Testing Models/02. 02 Intro SC V1-mIgABrjJVBY.en.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.zh-CN.vtt

1.5 kB

Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.pt-BR.vtt

1.5 kB

Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.pt-BR.vtt

1.5 kB

Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt

1.5 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.en.vtt

1.5 kB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.zh-CN.vtt

1.5 kB

Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.en.vtt

1.5 kB

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.pt-BR.vtt

1.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.pt-BR.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.en.vtt

1.5 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.en.vtt

1.5 kB

Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.pt-BR.vtt

1.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.zh-CN.vtt

1.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.en.vtt

1.5 kB

Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.es-MX.vtt

1.5 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.zh-CN.vtt

1.5 kB

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.pt-BR.vtt

1.5 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.zh-CN.vtt

1.5 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.pt-BR.vtt

1.5 kB

Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.pt-BR.vtt

1.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.pt-BR.vtt

1.5 kB

Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.en.vtt

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.en-US.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.zh-CN.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/09. Regularization-QcJBhbuCl5g.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.es-MX.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.pt-BR.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.zh-CN.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.pt-BR.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en.vtt

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Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.zh-CN.vtt

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Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.zh-CN.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.en-US.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.en-US.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.zh-CN.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.en.vtt

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.zh-CN.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.en-US.vtt

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Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.en.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.zh-CN.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/16. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.pt-BR.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.ar.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.en.vtt

1.6 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.en.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.en-US.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.en.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.en.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.pt-BR.vtt

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Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.ar.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/06. MLND - Unsupervised Learning - L3 06 GMM In 2D MAIN Sfx V1 V1-GsNWVHmRRG4.pt-BR.vtt

1.6 kB

Part 03-Module 01-Lesson 01_Linear Regression/img/f6.gif

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Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.zh-CN.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.pt-BR.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.zh-CN.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.en.vtt

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Part 04-Module 02-Lesson 03_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 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.zh-CN.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.ar.vtt

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.zh-CN.vtt

1.6 kB

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.en.vtt

1.6 kB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.zh-CN.vtt

1.6 kB

Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt

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Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.zh-CN.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.zh-CN.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.zh-CN.vtt

1.6 kB

Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.en-US.vtt

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Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.en.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.zh-CN.vtt

1.7 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.en.vtt

1.7 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.zh-CN.vtt

1.7 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/05. MLND - Unsupervised Learning - L3 05 Gaussian Distribution In 2D MAIN V1 V2-Ne-qRjO38qQ.zh-CN.vtt

1.7 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.pt-BR.vtt

1.7 kB

Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.zh-CN.vtt

1.7 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.pt-BR.vtt

1.7 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-weight-update.gif

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.en.vtt

1.7 kB

Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.pt-BR.vtt

1.7 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.zh-CN.vtt

1.7 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt

1.7 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.en.vtt

1.7 kB

Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en.vtt

1.7 kB

Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.pt-BR.vtt

1.7 kB

Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.en-US.vtt

1.7 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.en.vtt

1.7 kB

Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.pt-BR.vtt

1.7 kB

Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.pt-BR.vtt

1.7 kB

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.ar.vtt

1.7 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.pt-BR.vtt

1.7 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt

1.7 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt

1.7 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.zh-CN.vtt

1.7 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.pt-BR.vtt

1.7 kB

Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.pt-BR.vtt

1.7 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.en.vtt

1.7 kB

Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en.vtt

1.7 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.pt-BR.vtt

1.7 kB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.ar.vtt

1.7 kB

Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.pt-BR.vtt

1.7 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.pt-BR.vtt

1.7 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.pt-BR.vtt

1.7 kB

Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.en.vtt

1.7 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.zh-CN.vtt

1.7 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.pt-BR.vtt

1.7 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.pt-BR.vtt

1.7 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.zh-CN.vtt

1.7 kB

Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.zh-CN.vtt

1.7 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-layer-weights.gif

1.7 kB

Part 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.ar.vtt

1.7 kB

Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.zh-CN.vtt

1.8 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt

1.8 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt

1.8 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.en.vtt

1.8 kB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.en.vtt

1.8 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.en.vtt

1.8 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/08. MLND - Unsupervised Learning - L3 08 Overview Of The Expectation Maximization Algorithm MAIN V1 V1-XdQfFnnj5Xo.en.vtt

1.8 kB

Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.en.vtt

1.8 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.zh-CN.vtt

1.8 kB

Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.en.vtt

1.8 kB

Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.zh-CN.vtt

1.8 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.en.vtt

1.8 kB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.pt-BR.vtt

1.8 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.pt-BR.vtt

1.8 kB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.zh-CN.vtt

1.8 kB

Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.pt-BR.vtt

1.8 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.zh-CN.vtt

1.8 kB

Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.pt-BR.vtt

1.8 kB

Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.en.vtt

1.8 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.pt-BR.vtt

1.8 kB

Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.zh-CN.vtt

1.8 kB

Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.en.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt

1.8 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt

1.8 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.es-MX.vtt

1.8 kB

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.ar.vtt

1.8 kB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt

1.8 kB

Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.zh-CN.vtt

1.8 kB

Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.en-US.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.en.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.zh-CN.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.en.vtt

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Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.zh-CN.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.es-MX.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.en-US.vtt

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Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.zh-CN.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.ja-JP.vtt

1.8 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.en.vtt

1.8 kB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.zh-CN.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.en.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt

1.9 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.pt-BR.vtt

1.9 kB

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.ar.vtt

1.9 kB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.en.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.zh-CN.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.pt-BR.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.pt-BR.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.zh-CN.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.pt-BR.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/img/f2.gif

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Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.zh-CN.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.en.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.pt-BR.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.pt-BR.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.zh-CN.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.es-MX.vtt

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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.zh-CN.vtt

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Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.en-US.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.zh-CN.vtt

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Part 04-Module 02-Lesson 04_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 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.zh-CN.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.ar.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.en.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.ar.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.zh-CN.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt

1.9 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.es-MX.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt

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Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.zh-CN.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.zh-CN.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.pt-BR.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.zh-CN.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.pt-BR.vtt

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Part 04-Module 02-Lesson 04_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 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt

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Part 04-Module 02-Lesson 03_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 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.en.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en.vtt

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Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.en-US.vtt

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Part 04-Module 02-Lesson 03_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 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.en-US.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.es-MX.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.pt-BR.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.pt-BR.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.pt-BR.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/img/f1.gif

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Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.pt-BR.vtt

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Part 04-Module 02-Lesson 04_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 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt

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Part 04-Module 02-Lesson 04_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 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.pt-BR.vtt

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Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.pt-BR.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.pt-BR.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.pt-BR.vtt

2.0 kB

Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.zh-CN.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.pt-BR.vtt

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.zh-CN.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.en-US.vtt

2.1 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.zh-CN.vtt

2.1 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.en.vtt

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.ar.vtt

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.ar.vtt

2.1 kB

Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.pt-BR.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.en.vtt

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Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/img/codecogseqn-61.gif

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.en.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.en.vtt

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Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.es-MX.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt

2.1 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt

2.1 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.zh-CN.vtt

2.1 kB

Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.en.vtt

2.1 kB

Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.en-US.vtt

2.1 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.zh-CN.vtt

2.1 kB

Part 05-Module 01-Lesson 01_Neural Networks/img/codecogseqn-49.gif

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Part 05-Module 01-Lesson 03_Deep Neural Networks/img/sigmoid-derivative.gif

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.zh-CN.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.zh-CN.vtt

2.1 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.zh-CN.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.zh-CN.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.es-MX.vtt

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Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.ar.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.zh-CN.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.zh-CN.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.pt-BR.vtt

2.1 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.zh-CN.vtt

2.1 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt

2.1 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt

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Part 04-Module 02-Lesson 04_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 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.pt-BR.vtt

2.1 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.zh-CN.vtt

2.1 kB

Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.ar.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.zh-CN.vtt

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Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.pt-BR.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.en.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.en.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.ar.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.zh-CN.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.en.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.pt-BR.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt

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Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-general.gif

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Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.zh-CN.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.en.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.en.vtt

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Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.en.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.zh-CN.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.zh-CN.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.pt-BR.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.pt-BR.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/02. Sets and Maps-gmIb-qZhTDQ.en-US.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.zh-CN.vtt

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Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.zh-CN.vtt

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Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en-US.vtt

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Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.en.vtt

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Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.zh-CN.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.zh-CN.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.es-MX.vtt

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Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.zh-CN.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.pt-BR.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/codecogseqn-2.png

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Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en.vtt

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.en-US.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.ar.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.zh-CN.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.ar.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.pt-BR.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.pt-BR.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/01. MLND - Unsupervised Learning - L3 01 Gaussian Mixture Model MAINv1 V3-SLdZrt0CvOk.en.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.en.vtt

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Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt

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Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.en.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.en-US.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.en.vtt

2.3 kB

Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.en-US.vtt

2.3 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.en.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en.vtt

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Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.ar.vtt

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Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.en-US.vtt

2.3 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.ar.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.zh-CN.vtt

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Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.zh-CN.vtt

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Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.pt-BR.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.en.vtt

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Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.en.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.pt-BR.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.en.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.zh-CN.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.zh-CN.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.zh-CN.vtt

2.3 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.en.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.zh-CN.vtt

2.3 kB

Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.en-US.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.zh-CN.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt

2.4 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.en.vtt

2.4 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt

2.4 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.en.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.pt-BR.vtt

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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.zh-CN.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.pt-BR.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.pt-BR.vtt

2.4 kB

Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en.vtt

2.4 kB

Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.pt-BR.vtt

2.4 kB

Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.en-US.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

2.4 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.en.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.pt-BR.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.pt-BR.vtt

2.4 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt

2.4 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt

2.4 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.zh-CN.vtt

2.4 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.pt-BR.vtt

2.4 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.en.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.zh-CN.vtt

2.4 kB

Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.pt-BR.vtt

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Part 04-Module 02-Lesson 04_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 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.pt-BR.vtt

2.4 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.pt-BR.vtt

2.4 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.en-US.vtt

2.4 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.zh-CN.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.pt-BR.vtt

2.4 kB

Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.pt-BR.vtt

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.pt-BR.vtt

2.4 kB

Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.en.vtt

2.4 kB

Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.es-MX.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.zh-CN.vtt

2.4 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.zh-CN.vtt

2.4 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.en.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en.vtt

2.5 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.zh-CN.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.en-US.vtt

2.5 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.pt-BR.vtt

2.5 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.en.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.zh-CN.vtt

2.5 kB

Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

2.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.pt-BR.vtt

2.5 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt

2.5 kB

Part 04-Module 02-Lesson 04_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 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en.vtt

2.5 kB

Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.pt-BR.vtt

2.5 kB

Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.pt-BR.vtt

2.5 kB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.en.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.en-US.vtt

2.5 kB

Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.en.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.pt-BR.vtt

2.5 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.zh-CN.vtt

2.5 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.pt-BR.vtt

2.5 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.zh-CN.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.en-US.vtt

2.5 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.zh-CN.vtt

2.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.zh-CN.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.en.vtt

2.5 kB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.pt-BR.vtt

2.5 kB

Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.pt-BR.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.pt-BR.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.pt-BR.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.en-US.vtt

2.5 kB

Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en.vtt

2.5 kB

Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.en-US.vtt

2.5 kB

Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.zh-CN.vtt

2.5 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt

2.5 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt

2.5 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.en.vtt

2.5 kB

Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.zh-CN.vtt

2.5 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt

2.5 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt

2.5 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.es-MX.vtt

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Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.en.vtt

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Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.en-US.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.zh-CN.vtt

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Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.pt-BR.vtt

2.5 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.en.vtt

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Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.zh-CN.vtt

2.6 kB

Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.ar.vtt

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Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.pt-BR.vtt

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Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.pt-BR.vtt

2.6 kB

Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.zh-CN.vtt

2.6 kB

Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.zh-CN.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt

2.6 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt

2.6 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.zh-CN.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.en.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.en.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.es-MX.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.en.vtt

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Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.en.vtt

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Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.zh-CN.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.en.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.zh-CN.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.zh-CN.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.es-MX.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.zh-CN.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.en.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.en.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.en.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.en-US.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.pt-BR.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.pt-BR.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.en.vtt

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Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.pt-BR.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.en.vtt

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.pt-BR.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.pt-BR.vtt

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.zh-CN.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/hidden-errors.gif

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Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.zh-CN.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.en.vtt

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Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.en.vtt

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Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.zh-CN.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.pt-BR.vtt

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.en.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.en.vtt

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.zh-CN.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.pt-BR.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/weight-label-reference.gif

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Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.pt-BR.vtt

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Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.en.vtt

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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.en.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en.vtt

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Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.en-US.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.pt-BR.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.en.vtt

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Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.zh-CN.vtt

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Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.pt-BR.vtt

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Part 04-Module 02-Lesson 04_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 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.zh-CN.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.zh-CN.vtt

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Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.zh-CN.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.en.vtt

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Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.zh-CN.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.zh-CN.vtt

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Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.zh-CN.vtt

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.zh-CN.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.pt-BR.vtt

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Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.ar.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/backprop-error.gif

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Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.en.vtt

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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.pt-BR.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.en.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.en.vtt

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Part 04-Module 02-Lesson 04_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 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.pt-BR.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.pt-BR.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.en.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.pt-BR.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.en.vtt

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Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.en.vtt

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Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.zh-CN.vtt

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Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.ar.vtt

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.zh-CN.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.pt-BR.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.zh-CN.vtt

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.en-US.vtt

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Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.zh-CN.vtt

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.en-US.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.ar.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.pt-BR.vtt

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Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.zh-CN.vtt

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.zh-CN.vtt

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Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.en.vtt

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.en.vtt

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Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.en.vtt

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Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.ar.vtt

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Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.en.vtt

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.pt-BR.vtt

3.1 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.en.vtt

3.1 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.zh-CN.vtt

3.1 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.en.vtt

3.1 kB

Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.es-MX.vtt

3.1 kB

Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.pt-BR.vtt

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.pt-BR.vtt

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Part 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.pt-BR.vtt

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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt

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Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.en.vtt

3.1 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.zh-CN.vtt

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Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.pt-BR.vtt

3.1 kB

Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.pt-BR.vtt

3.1 kB

Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.pt-BR.vtt

3.1 kB

Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.pt-BR.vtt

3.1 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.en.vtt

3.1 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.zh-CN.vtt

3.1 kB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.zh-CN.vtt

3.1 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.pt-BR.vtt

3.1 kB

Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.pt-BR.vtt

3.1 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

3.1 kB

Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt

3.1 kB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.ar.vtt

3.1 kB

Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.pt-BR.vtt

3.1 kB

Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en.vtt

3.2 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.pt-BR.vtt

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Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.en-US.vtt

3.2 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en.vtt

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Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en.vtt

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Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.en-US.vtt

3.2 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.en-US.vtt

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Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.en.vtt

3.2 kB

Part 04-Module 02-Lesson 04_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 03-Module 01-Lesson 06_Ensemble Methods/02. MLND SL EM 02 Bagging V1 MAIN V1-9L_B0Jcio3c.en.vtt

3.2 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.pt-BR.vtt

3.2 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.pt-BR.vtt

3.2 kB

Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en.vtt

3.2 kB

Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.en-US.vtt

3.2 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.es-MX.vtt

3.2 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/mse.png

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Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.en.vtt

3.2 kB

Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.en.vtt

3.2 kB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.es-MX.vtt

3.2 kB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.pt-BR.vtt

3.2 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.zh-CN.vtt

3.2 kB

Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.en.vtt

3.2 kB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.zh-CN.vtt

3.2 kB

Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.pt-BR.vtt

3.2 kB

Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en.vtt

3.2 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.en.vtt

3.3 kB

Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.en-US.vtt

3.3 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.zh-CN.vtt

3.3 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt

3.3 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt

3.3 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.zh-CN.vtt

3.3 kB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.pt-BR.vtt

3.3 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

3.3 kB

Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt

3.3 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.pt-BR.vtt

3.3 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.pt-BR.vtt

3.3 kB

Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.en.vtt

3.3 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en.vtt

3.3 kB

Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.en-US.vtt

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Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en.vtt

3.3 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.pt-BR.vtt

3.3 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.zh-CN.vtt

3.3 kB

Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.en-US.vtt

3.3 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.pt-BR.vtt

3.3 kB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/img/heaviside-step-function-2.gif

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Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.zh-CN.vtt

3.3 kB

Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.pt-BR.vtt

3.3 kB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.en.vtt

3.3 kB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.zh-CN.vtt

3.3 kB

Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.en-US.vtt

3.3 kB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt

3.3 kB

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.ar.vtt

3.3 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt

3.3 kB

Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.pt-BR.vtt

3.3 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.zh-CN.vtt

3.3 kB

Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.en.vtt

3.3 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.zh-CN.vtt

3.3 kB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.pt-BR.vtt

3.4 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

3.4 kB

Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt

3.4 kB

Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.pt-BR.vtt

3.4 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.pt-BR.vtt

3.4 kB

Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en.vtt

3.4 kB

Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.en-US.vtt

3.4 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.zh-CN.vtt

3.4 kB

Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.en.vtt

3.4 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.zh-CN.vtt

3.4 kB

Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.pt-BR.vtt

3.4 kB

Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.pt-BR.vtt

3.4 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.en.vtt

3.4 kB

Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt

3.4 kB

Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.pt-BR.vtt

3.4 kB

Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.en.vtt

3.4 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.en.vtt

3.4 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.en.vtt

3.4 kB

Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.en.vtt

3.4 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.zh-CN.vtt

3.4 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt

3.4 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.zh-CN.vtt

3.4 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.pt-BR.vtt

3.4 kB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt

3.4 kB

Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.zh-CN.vtt

3.4 kB

Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.pt-BR.vtt

3.4 kB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt

3.4 kB

Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en.vtt

3.4 kB

Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.en-US.vtt

3.4 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.pt-BR.vtt

3.4 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.en.vtt

3.5 kB

Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

3.5 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt

3.5 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.pt-BR.vtt

3.5 kB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.en.vtt

3.5 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.en.vtt

3.5 kB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.en.vtt

3.5 kB

Part 11-Module 01-Lesson 01_Software and Tools/index.html

3.5 kB

Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.pt-BR.vtt

3.5 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.pt-BR.vtt

3.5 kB

Part 10-Module 01-Lesson 04_Land a Job Offer/index.html

3.5 kB

Part 04-Module 05-Lesson 01_PCA Mini-Project/index.html

3.5 kB

Part 05-Module 01-Lesson 06_Deep Learning Assessment/index.html

3.5 kB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.zh-CN.vtt

3.5 kB

Part 02-Module 04-Lesson 01_NumPy and pandas Assessment/index.html

3.5 kB

Part 04-Module 07-Lesson 01_Unsupervised Learning Assessment/index.html

3.5 kB

Part 06-Module 03-Lesson 01_Reinforcement Learning Assessment/index.html

3.5 kB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

3.5 kB

Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt

3.5 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.pt-BR.vtt

3.5 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en.vtt

3.5 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en.vtt

3.5 kB

Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.pt-BR.vtt

3.5 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.en-US.vtt

3.5 kB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.en-US.vtt

3.5 kB

Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.en.vtt

3.5 kB

Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.zh-CN.vtt

3.5 kB

Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.en.vtt

3.5 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en.vtt

3.5 kB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt

3.5 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.en-US.vtt

3.5 kB

Part 03-Module 01-Lesson 07_Supervised Learning Assessment/index.html

3.5 kB

Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.zh-CN.vtt

3.5 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.en.vtt

3.5 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.en.vtt

3.5 kB

Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.en.vtt

3.5 kB

Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.pt-BR.vtt

3.5 kB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.es-MX.vtt

3.6 kB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.zh-CN.vtt

3.6 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.pt-BR.vtt

3.6 kB

Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.zh-CN.vtt

3.6 kB

Part 02-Module 04-Lesson 02_Model Evaluation and Validation Assessment/index.html

3.6 kB

Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.en.vtt

3.6 kB

Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt

3.6 kB

Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.zh-CN.vtt

3.6 kB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.en.vtt

3.6 kB

Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.zh-CN.vtt

3.6 kB

Part 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.zh-CN.vtt

3.6 kB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.pt-BR.vtt

3.6 kB

Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.zh-CN.vtt

3.6 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en.vtt

3.6 kB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt

3.6 kB

Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.zh-CN.vtt

3.6 kB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.en-US.vtt

3.6 kB

Part 10-Module 01-Lesson 01_Ace Your Interview/index.html

3.6 kB

Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/index.html

3.6 kB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.pt-BR.vtt

3.6 kB

Part 04-Module 02-Lesson 02_Clustering Mini-Project/index.html

3.6 kB

Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.zh-CN.vtt

3.6 kB

Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.en.vtt

3.6 kB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.en.vtt

3.6 kB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.zh-CN.vtt

3.7 kB

Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt

3.7 kB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.es-MX.vtt

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Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/index.html

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Part 06-Module 02-Lesson 03_Policy-Based Methods/index.html

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Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/index.html

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Part 04-Module 08-Lesson 01_Creating Customer Segments/index.html

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Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/index.html

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/index.html

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Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network.html

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Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/02. Quadcopter workspace.html

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Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction.html

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Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Precision and Recall.html

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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components.html

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely.html

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction.html

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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt

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Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. Welcome to the Machine Learning Engineer Nanodegree Program.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation.html

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Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion.html

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Part 02-Module 03-Lesson 01_Model Selection/05. Learning Curves.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces.html

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Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team.html

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Part 04-Module 06-Lesson 01_Random Projection and ICA/08. [Lab] Independent Component Analysis.html

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Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort.html

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Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort.html

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Part 04-Module 06-Lesson 01_Random Projection and ICA/09. [Solution] Independent Component Analysis.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation.html

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Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets.html

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Part 03-Module 01-Lesson 04_Naive Bayes/01. Intro.html

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.en.vtt

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Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning.html

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Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network.html

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Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro.html

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Part 01-Module 01-Lesson 03_Introductory Practice Project/02. Software Requirements.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.pt-BR.vtt

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Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/01. Project Overview.html

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Part 09-Module 01-Lesson 03_Udacity Professional Profile/06. Skills.html

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Part 03-Module 01-Lesson 04_Naive Bayes/02. Guess the Person.html

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Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn.html

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Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort.html

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Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort.html

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Part 03-Module 01-Lesson 04_Naive Bayes/03. Known and Inferred.html

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Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search.html

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences.html

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Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.pt-BR.vtt

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Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay.html

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0).html

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Part 03-Module 01-Lesson 05_Support Vector Machines/18. Outro.html

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0).html

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company.html

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Part 03-Module 01-Lesson 04_Naive Bayes/07. Solution False Positives.html

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Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.pt-BR.vtt

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Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/Project Description - Teach a Quadcopter How to Fly.html

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences.html

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Part 01-Module 01-Lesson 03_Introductory Practice Project/03. Project files.html

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Part 03-Module 01-Lesson 04_Naive Bayes/09. Bayesian Learning 2.html

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Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.en.vtt

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Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search.html

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Part 03-Module 01-Lesson 08_Supervised Learning Project/01. Overview.html

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Part 04-Module 04-Lesson 01_PCA/index.html

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure.html

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding.html

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Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation.html

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Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.en.vtt

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Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.zh-CN.vtt

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Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.zh-CN.vtt

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Part 09-Module 01-Lesson 02_LinkedIn Review/02. Resources in Your Career Portal.html

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Part 03-Module 01-Lesson 04_Naive Bayes/12. Naive Bayes Algorithm 2.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging.html

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries.html

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Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/17. MLND - Unsupervised Learning - L3 18 External Validation Indices MAIN V1 V2-rXZM5X2-5D0.en.vtt

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Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases.html

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Part 10-Module 02-Lesson 05_Trees/13. BST Complications.html

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assets/css/fonts/KaTeX_Size4-Regular.woff

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/08. Exercise Tile Coding.html

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Part 10-Module 01-Lesson 02_Practice Behavioral Questions/03. Analyzing Behavioral Answers.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming.html

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.ar.vtt

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!.html

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences.html

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Part 08-Module 01-Lesson 01_Conduct a Job Search/05. Resources in Your Career Portal.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.pt-BR.vtt

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/06. Exercise Discretization.html

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Part 04-Module 06-Lesson 01_Random Projection and ICA/04. Independent Component Analysis (ICA).html

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Part 09-Module 01-Lesson 02_LinkedIn Review/Project Description - LinkedIn Profile Review Project.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/16. RBF Kernel 3.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/07. Margin Error.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/01. Intro.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/09. Error Function.html

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Part 06-Module 02-Lesson 02_Deep Q-Learning/12. TensorFlow Implementation.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/10. The C Parameter.html

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks.html

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Part 07-Module 01-Lesson 01_Writing up a Capstone Proposal/05. Submitting the Project.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/04. Error Function Intuition.html

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis.html

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Part 02-Module 03-Lesson 01_Model Selection/10. Grid Search Lab.html

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Part 01-Module 01-Lesson 01_Welcome to Machine Learning/07. Program Readiness.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/11. Polynomial Kernel 1.html

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/index.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/03. Minimizing Distances.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/06. Classification Error.html

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Part 10-Module 01-Lesson 01_Ace Your Interview/03. STAR Method.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction.html

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Part 10-Module 01-Lesson 05_Interview Practice/03. Analyzing an Interview.html

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Part 02-Module 03-Lesson 01_Model Selection/11. [Solution] Grid Search Lab.html

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Part 09-Module 01-Lesson 03_Udacity Professional Profile/01. Introduction.html

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.zh-CN.vtt

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review.html

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Part 11-Module 04-Lesson 01_Deep Neural Networks/02. Two-Layer Neural Network.html

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Part 10-Module 02-Lesson 05_Trees/12. BSTs.html

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Part 04-Module 02-Lesson 04_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 10-Module 02-Lesson 05_Trees/15. Heaps.html

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Part 04-Module 02-Lesson 01_Clustering/13. Sklearn.html

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction.html

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Part 09-Module 01-Lesson 03_Udacity Professional Profile/02. Getting Started.html

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Part 10-Module 02-Lesson 05_Trees/16. Heapify.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.en.vtt

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression Answer.html

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Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.en.vtt

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Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/04. Uploading to Workspace.html

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Part 04-Module 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.pt-BR.vtt

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Part 05-Module 01-Lesson 02_Cloud Computing/01. Overview.html

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Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning.html

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review.html

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Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations.html

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Part 04-Module 02-Lesson 01_Clustering/09. Handoff to Katie.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2.html

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Part 04-Module 02-Lesson 04_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 02_The RL Framework The Problem/17. MDPs, Part 3.html

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/Project Description - Resume Review Project (Career Change).html

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Part 04-Module 08-Lesson 01_Creating Customer Segments/05. Uploading to Workspace.html

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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt

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Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies.html

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Part 03-Module 01-Lesson 04_Naive Bayes/04. Guess the Person Now.html

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Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology.html

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete.html

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Part 06-Module 02-Lesson 05_Teach a Quadcopter How to Fly/03. Replay Buffer.html

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Part 02-Module 05-Lesson 01_Predicting Boston Housing Prices/02. Starting the project.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward.html

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Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills.html

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Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation.html

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Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees.html

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa.html

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Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning.html

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Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.en.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax.html

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Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees.html

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Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.en.vtt

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Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy.html

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Part 03-Module 01-Lesson 03_Decision Trees/20. Outro.html

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Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/06. The Reward Hypothesis.html

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/08. Resources in Your Career Portal.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited.html

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. K-means considerations.html

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/17. Further Reading.html

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.en.vtt

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Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means.html

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format.html

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Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.en.vtt

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Part 03-Module 01-Lesson 04_Naive Bayes/15. Spam Classifier - Workspace.html

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1.html

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Part 04-Module 08-Lesson 01_Creating Customer Segments/03. Starting the project.html

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Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.zh-CN.vtt

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Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values.html

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Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. Overview of other clustering methods.html

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/Project Description - Resume Review Project (Prior Industry Experience).html

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Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-AF07y1oAim0.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/04. Accuracy 2-ueYCLfd_aNQ.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/13. SVM Question-Fwnjx0s_AIw.mp4

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Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/go.jpg

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/img/screen-shot-2017-09-24-at-4.28.04-pm.png

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Part 03-Module 01-Lesson 06_Ensemble Methods/img/screen-shot-2018-01-03-at-2.20.30-pm.png

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Part 03-Module 01-Lesson 01_Linear Regression/14. Absolute Vs Squared Error-csvdjaqt1GM.mp4

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Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-qPr3Uj55eog.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Question-lp1NrLZnCUM.mp4

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Part 06-Module 01-Lesson 05_Monte Carlo Methods/img/screen-shot-2017-10-04-at-4.58.58-pm.png

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/12. 13 L One Hot Encoding-phYsxqlilUk.mp4

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Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-uC1Xwc7warg.mp4

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Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-terminal.gif

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/02. Color-Question-BdQccpMwk80.mp4

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Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-Thj7e55iSlA.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/13. 13 Quiz Sensitivity And Specificty V3-O17MnhWBmKA.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/19. Learning Rate-TwJ8aSZoh2U.mp4

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Part 06-Module 01-Lesson 04_Dynamic Programming/img/statevalue.png

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Part 03-Module 01-Lesson 01_Linear Regression/03. Solution Housing Prices-uhdTulw9-Nc.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/16. 15 Quiz Diagnosing Cancer V3-4UzkwecBJro.mp4

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Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-ZMfwPUrOFsE.mp4

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Part 08-Module 01-Lesson 01_Conduct a Job Search/04. Open Yourself Up to Opportunity-1OamTNkk1xM.mp4

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Part 04-Module 06-Lesson 01_Random Projection and ICA/03. L6 2 Random Projection Impl MAINv1 V1 V1-5DhvurLgRII.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/28. Mini Project Introduction-Rgf3YVFWl-M.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/07. Feature-Map-Sizes-Solution-W4xtf8LTz1c.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/convolutionalnetworksquiz.png

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Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-l6YXxmCNtHk.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/arch.png

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Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 08-Module 03-Lesson 01_Craft Your Cover Letter/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 09-Module 01-Lesson 03_Udacity Professional Profile/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 09-Module 01-Lesson 01_Develop Your Personal Brand/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 10-Module 02-Lesson 08_Technical Interview - Python/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 09-Module 01-Lesson 02_LinkedIn Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 09-Module 02-Lesson 01_GitHub Review/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 10-Module 01-Lesson 05_Interview Practice/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 08-Module 01-Lesson 01_Conduct a Job Search/img/screen-shot-2017-10-31-at-1.06.42-pm.png

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/09. Training The Neural Network-HwiI-UXUx-M.mp4

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/07. Supervised Classification-XTGsutypAPE.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/12. Non-Linear Regions-B8UrWnHh1Wc.mp4

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Part 04-Module 02-Lesson 01_Clustering/07. Moving Centers 2-FY0DXe0lfrI.mp4

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Part 02-Module 03-Lesson 01_Model Selection/12. Outro SC V1-YD1grQje9fw.mp4

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Part 03-Module 01-Lesson 04_Naive Bayes/12. MLND SL NB Solution Naive Bayes Algorithm-QDj3xzjuYmo.mp4

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/03. Let'S Get Started-ySIDqaXLhHw.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Chain Rule-YAhIBOnbt54.mp4

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Part 04-Module 03-Lesson 01_Feature Scaling/05. Chris's Shirt Size by Our Metric-oWyt6md7P44.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/06. 06 Image Challenge V3-Efnoj1KNPHw.mp4

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Part 03-Module 01-Lesson 01_Linear Regression/02. DLND REG 01 Quiz Housing Prices V2-8CSBiVKu35Q.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/08. Solution Data Challenges-1z3o4niQuNg.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/23. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4

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Part 06-Module 01-Lesson 04_Dynamic Programming/img/frozen-lake-6.jpg

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Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-FpQm_dYA9LM.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/03. Survival Rate-QPlp3NeGuSk.mp4

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Part 03-Module 01-Lesson 05_Support Vector Machines/02. SVM 01 Which Line Is Better V1-NCml_NCvd1I.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/01. Introduction-ZCpXvVdIdnY.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/lesions.png

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Part 03-Module 01-Lesson 01_Linear Regression/23. Conclusion-pyeojf0NniQ.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/14. Solution Sensitivty And Specificity-GBZjyeMjKxc.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/10. 10 Quiz Random Vs Preinitiliazed Weights V3-DRC1e4XGl2M.mp4

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Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-8Ygq5dRV0Kk.mp4

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Part 04-Module 03-Lesson 01_Feature Scaling/10. MinMax Rescaler Coding Quiz-ePXAzoGVviM.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/02. Decision Trees Question-1RonLycEJ34.mp4

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Part 09-Module 02-Lesson 01_GitHub Review/05. Identify fixes for example “bad” profile-ncFtwW5urHk.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/01. Intro to CNNs-B61jxZ4rkMs.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/08. Convolutions Cont.-utOv-BKI_vo.mp4

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Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-lS5DfbsWH34.mp4

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/03. Classification Example-46PywnGa_cQ.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/04. Classification Example-46PywnGa_cQ.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/img/skin-disease-classes.png

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Part 05-Module 01-Lesson 01_Neural Networks/17. One-Hot Encoding-AePvjhyvsBo.mp4

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Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-4hJlaYRHdpA.mp4

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Part 03-Module 01-Lesson 05_Support Vector Machines/09. SVM 07 Error Function V1-A1wbrcSYc1c.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/16. Quiz - Softmax-NNoezNnAMTY.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/23. Error Functions Around the World-34AAcTECu2A.mp4

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Part 10-Module 02-Lesson 05_Trees/13. BST Complications-pcB0wV7myy4.mp4

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Part 02-Module 03-Lesson 01_Model Selection/04. KFold Cross Validation V3 V1-9W6o6eWGi-0.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/09. Linear Regression Question-sf51L0RN6zc.mp4

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Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-TN1rQMrx65c.mp4

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Part 03-Module 01-Lesson 04_Naive Bayes/09. SL NB 08 S Bayesian Learning 2 V1 V6-3rIYZgCXVXY.mp4

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Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-oOUx6NHppdQ.mp4

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Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-1ask5zHGQKM.mp4

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Part 03-Module 01-Lesson 01_Linear Regression/10. Mean Squared Error-MRyxmZDngI4.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz Cross Entropy-njq6bYrPqSU.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Multiclass Classification-uNTtvxwfox0.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/10. Perceptron Algorithm--zhTROHtscQ.mp4

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. Perceptron Algorithm--zhTROHtscQ.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 S Softmax-n8S-v_LCTms.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/25. Gradient Descent Algorithm-snxmBgi_GeU.mp4

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Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-xSQTzAeeoEc.mp4

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Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/run-main.gif

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Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-DX_f02bUHT0.mp4

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Part 02-Module 03-Lesson 01_Model Selection/13. MLND Outro-sFvMBncQjr8.mp4

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Part 05-Module 01-Lesson 04_Convolutional Neural Networks/01. Introducing Alexis-38ExGpdyvJI.mp4

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Part 10-Module 02-Lesson 07_Case Studies in Algorithms/02. Shortest Path Problem-huKUM97Vve8.mp4

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Part 03-Module 01-Lesson 02_Perceptron Algorithm/02. Classsification Example-Dh625piH7Z0.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/03. Classsification Example-Dh625piH7Z0.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/21. Formula For Cross 1-qvr_ego_d6w.mp4

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Part 10-Module 02-Lesson 04_Maps and Hashing/08. Hash Maps-A-ahUVi8pYQ.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/01. Non-Linear Data-F7ZiE8PQiSc.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/21. Momentum-r-rYz_PEWC8.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/09. 07 Recall SC V1-0n5wUZiefkQ.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/05. When Accuracy Wont Work-r0-O-gIDXZ0.mp4

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Part 03-Module 01-Lesson 03_Decision Trees/12. MLND SL DT 10 Q Information Gain MAIN V1-tVLOLPEtLFw.mp4

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Part 03-Module 01-Lesson 06_Ensemble Methods/03. MLND SL EM 03 AdaBoost V1 MAIN V1-HD6SRBWKGUE.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/26. Keras Lab-a50un22BsLI.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/04. Medical Classification-RCOSP60dV7U.mp4

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Part 11-Module 05-Lesson 01_Convolutional Neural Networks/03. Statistical Invariance-0Hr5YwUUhr0.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/06. 04 Quiz False Negatives And Positives SC V1-_ytP9zIkziw.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/07. Answer False Negatives And Positives-KOytJL1lvgg.mp4

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/23. 32 L Parameter Hyperspace!-5a3-iIhdguc.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/08. 06 Precision SC V1-q2wVorBfefU.mp4

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Part 09-Module 02-Lesson 01_GitHub Review/07. Quick Fixes #2-It6AEuSDQw0.mp4

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Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs Continuous-rdP-RPDFkl0.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/07. Naive Bayes Answer-YKN-fjuZ1VU.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/20. Recap and Challenge-ecREasTrKu4.mp4

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Part 05-Module 01-Lesson 03_Deep Neural Networks/17. Other Activation Functions-kA-1vUt6cvQ.mp4

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Part 11-Module 02-Lesson 01_Intro to TensorFlow/22. 31 L Momentum And Learning Rate Decay-O3QYdmQjXds.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/17. 16 Solution Diagnosing Cancer V3-IJYvt2ssUFk.mp4

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Part 10-Module 02-Lesson 05_Trees/08. Search and Delete-KbL-HK3ztX8.mp4

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Part 03-Module 01-Lesson 06_Ensemble Methods/11. Supervised Learning Outro V2-7X2SDqzGrdU.mp4

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Part 02-Module 02-Lesson 01_Evaluation Metrics/03. Accuracy-s6SfhPTNOHA.mp4

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Part 04-Module 04-Lesson 01_PCA/24. Maximum Number of PCs Quiz-q4c5n5W2aUc.mp4

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Part 06-Module 01-Lesson 03_The RL Framework The Solution/04. Gridworld Example-XeHBmPFqTsE.mp4

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Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-6ufIq2nrTwg.mp4

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Part 02-Module 01-Lesson 01_Training and Testing Models/08. MLND Turning Paramaters-eSv2lPcnRM0.mp4

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Part 03-Module 01-Lesson 05_Support Vector Machines/01. Support Vector Machine V2-LBmM6pZCrI0.mp4

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Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/22. Visualization-aGIGB4Ta3_A.mp4

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Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-U3FUxkm1MxI.mp4

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Part 01-Module 01-Lesson 02_What is Machine Learning/08. Gradient Descent-BEC0uH1fuGU.mp4

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Part 11-Module 03-Lesson 01_Intro to Neural Networks/10. Gradient Descent-29PmNG7fuuM.mp4

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Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-iY_sO4d23gY.mp4

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Part 04-Module 04-Lesson 01_PCA/14. Measurable vs. Latent Features Quiz-UeSD19oit_w.mp4

2.5 MB

Part 03-Module 01-Lesson 06_Ensemble Methods/04. MLND SL EM 04 Weighting The Data MAIN V1 V2-O-hh_x0iYW8.mp4

2.5 MB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-ntRkOeSZutw.mp4

2.5 MB

Part 10-Module 02-Lesson 05_Trees/16. Heapify-CAbDbiCfERY.mp4

2.5 MB

Part 11-Module 04-Lesson 01_Deep Neural Networks/05. Training a Deep Learning Network-CsB7yUtMJyk.mp4

2.6 MB

Part 03-Module 01-Lesson 01_Linear Regression/09. Mean Absolute Error-vLKiY0Ehors.mp4

2.6 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/29. Conclusion-wOiUQDgGD9E.mp4

2.6 MB

Part 11-Module 04-Lesson 01_Deep Neural Networks/11. Dropout RENDER-6DcImJS8uV8.mp4

2.6 MB

Part 05-Module 01-Lesson 01_Neural Networks/06. 09 Higher Dimensions-eBHunImDmWw.mp4

2.6 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/05. 09 Higher Dimensions-eBHunImDmWw.mp4

2.6 MB

Part 03-Module 01-Lesson 04_Naive Bayes/03. SL NB 02 Known And Inferred V1 V2-DrYfZXiDLQI.mp4

2.6 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/29. Inception Module-SlTm03bEOxA.mp4

2.6 MB

Part 03-Module 01-Lesson 06_Ensemble Methods/08. MLND SL EM 08 Combining The Models V1 MAIN V1-1GxscvKU2Ic.mp4

2.7 MB

Part 03-Module 01-Lesson 01_Linear Regression/16. Higher Dimensions--UvpQV1qmiE.mp4

2.7 MB

Part 02-Module 02-Lesson 01_Evaluation Metrics/11. 09 Quiz Fbeta Score SC V1-KSswld4_9bY.mp4

2.7 MB

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-WxAWorS2SLg.mp4

2.7 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/07. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.7 MB

Part 05-Module 01-Lesson 01_Neural Networks/08. AND And OR Perceptrons-45K5N0P9wJk.mp4

2.7 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/21. ROC Curve-fWwe_JlpnlQ.mp4

2.7 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/08. Training Your Logistic Classifier-WQsdr1EJgz8.mp4

2.7 MB

Part 02-Module 01-Lesson 01_Training and Testing Models/01. 01 Intro-4C4PuJANIdE.mp4

2.7 MB

Part 06-Module 01-Lesson 07_Solve OpenAI Gym's Taxi-v2 Task/img/open-agent-monitor-main.gif

2.7 MB

Part 04-Module 02-Lesson 01_Clustering/05. Match Points with Clusters-wJV1cRjmIYY.mp4

2.8 MB

Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-TbT6a6qaj08.mp4

2.8 MB

Part 04-Module 04-Lesson 01_PCA/22. Neighborhood Composite Feature-adXoa85rnPM.mp4

2.8 MB

Part 09-Module 02-Lesson 01_GitHub Review/12. Participating in open source projects-OxL-gMTizUA.mp4

2.8 MB

Part 10-Module 02-Lesson 06_Graphs/04. Connectivity-4x6u2KtNDg4.mp4

2.8 MB

Part 04-Module 02-Lesson 01_Clustering/04. How Many Clusters-R6oIvdBtsZw.mp4

2.8 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/10. Interview Wrap-Up-sz4Ekcu9a_Q.mp4

2.8 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/05. Naive Bayes Quiz-jsLkVYXmr3E.mp4

2.8 MB

Part 10-Module 02-Lesson 05_Trees/02. Tree Basics-oaxLPzaXRDc.mp4

2.8 MB

Part 04-Module 02-Lesson 01_Clustering/14. Some challenges of k-means-e2CdlG5P4WA.mp4

2.8 MB

Part 04-Module 04-Lesson 01_PCA/17. Composite Features-0ZBp8oWySAc.mp4

2.8 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4

2.8 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/06. Write the Conclusion-i3ozyhGPmIg.mp4

2.8 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/11. Solution Random Vs Preinitialized Thoughts-sOuoRZRKDzs.mp4

2.8 MB

Part 03-Module 01-Lesson 01_Linear Regression/18. Closed Form Solution-G3fRVgLa5gI.mp4

2.8 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/14. Multilayer perceptrons-Rs9petvTBLk.mp4

2.8 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/05. The Data-2RLbbV7MQNA.mp4

2.9 MB

Part 03-Module 01-Lesson 06_Ensemble Methods/07. MLND SL EM 07 Weighting The Models 3 V1 MAIN V1-fecp5nmetws.mp4

2.9 MB

Part 05-Module 01-Lesson 01_Neural Networks/11. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.9 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/09. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4

2.9 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png

2.9 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.08.11-pm.png

2.9 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/01. Introduction-X_9l_ZqXXBA.mp4

2.9 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/03. SVM 02 Minimizing Distances V1-mNKk2dBsNGA.mp4

2.9 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/10. Training Optimization-UiGKhx9pUYc.mp4

3.0 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/11. Logistic Regression Question-wQXKdeVHTmc.mp4

3.0 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/13. MLND - Unsupervised Learning - L3 13 GMM Implementation MAIN V1 V2-zWrC_2Npy9E.mp4

3.0 MB

Part 10-Module 02-Lesson 02_List-Based Collections/02. Lists-KUQSgUMtyv0.mp4

3.0 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/06. M2L3 06 V1-RMjdQkl6CqE.mp4

3.0 MB

Part 05-Module 01-Lesson 01_Neural Networks/19. Quiz - Cross 1--xxrisIvD0E.mp4

3.0 MB

Part 09-Module 02-Lesson 01_GitHub Review/11. Reflect on your commit messages-_0AHmKkfjTo.mp4

3.0 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/25. Confusion Matrix-3rpN-YYlfes.mp4

3.1 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/02. MLND - Unsupervised Learning - L2 02 V1-Ed6RKuBzKWA.mp4

3.1 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png

3.1 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.02-pm.png

3.1 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/06. RL M2L4 06 Actor Critic With Advantage RENDER V1 V1-Bwd2OF7hJXQ.mp4

3.1 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/17. Kernel Method Quiz-x0JqH6-Dhvw.mp4

3.1 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/14. 16 L Minimizing Cross-Entropy-YrDMXFhvh9E.mp4

3.1 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Layers-pg99FkXYK0M.mp4

3.1 MB

Part 04-Module 04-Lesson 01_PCA/08. Principal Axis of New Coordinate System-i6zv8vyZBk0.mp4

3.1 MB

Part 10-Module 02-Lesson 06_Graphs/13. Eulerian Path-zS34kHSo7fs.mp4

3.1 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/28. 1x1 Convolutions-Zmzgerm6SjA.mp4

3.2 MB

Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-_TJeoCTDykE.mp4

3.2 MB

Part 05-Module 01-Lesson 01_Neural Networks/28. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4

3.2 MB

Part 03-Module 01-Lesson 04_Naive Bayes/01. Naive Bayes Intro V2-vNOiQXghgRY.mp4

3.2 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/15. SVM Answer-JrUtTwfnsfM.mp4

3.3 MB

Part 04-Module 03-Lesson 01_Feature Scaling/09. Feature Scaling Formula Quiz 3-bY2fuRkH3iw.mp4

3.3 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/04. Syntax-08M93RaBSgU.mp4

3.3 MB

Part 03-Module 01-Lesson 01_Linear Regression/07. Square Trick-AGZEq-yQgRM.mp4

3.3 MB

Part 09-Module 02-Lesson 01_GitHub Review/14. Participating in open source projects 2-elZCLxVvJrY.mp4

3.3 MB

Part 05-Module 01-Lesson 01_Neural Networks/29. Neural Networks Outro V2-pwA5shUkRVc.mp4

3.3 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Calculating The Gradient 1 -tVuZDbUrzzI.mp4

3.3 MB

Part 04-Module 02-Lesson 01_Clustering/10. K-Means Cluster Visualization-iCTPBcowJRY.mp4

3.3 MB

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-JSVsHbGUuIE.mp4

3.3 MB

Part 02-Module 02-Lesson 01_Evaluation Metrics/13. Regression-Metrics-906P4BPnl9A.mp4

3.4 MB

Part 09-Module 02-Lesson 01_GitHub Review/02. GitHub profile important items-prvPVTjVkwQ.mp4

3.4 MB

Part 04-Module 03-Lesson 01_Feature Scaling/07. Feature Scaling Formula Quiz 1-jOxS1eJRsOk.mp4

3.4 MB

Part 03-Module 01-Lesson 06_Ensemble Methods/01. MLND SL EM 01 Intro V1 MAIN V2-5v9KqIo6CFE.mp4

3.4 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/20. 29 L Optimizing A Logistic Classifier-U_7nO1dm2tY.mp4

3.4 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/15. Backpropagation-MZL97-2joxQ.mp4

3.4 MB

Part 02-Module 03-Lesson 01_Model Selection/08. Grid Search SC V1-zDw-ZGiHW5I.mp4

3.4 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/02. Logistic Regression - Question-kSs6O3R7JUI.mp4

3.5 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/24. Neural Network Regression-aUJCBqBfEnI.mp4

3.5 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/23. What Is The Neural Network Looking At-qN-rvoxPbBw.mp4

3.5 MB

Part 09-Module 02-Lesson 01_GitHub Review/15. Starring interesting repositories-ZwMY5rAAd7Q.mp4

3.5 MB

Part 10-Module 02-Lesson 05_Trees/17. Heap Implementation-2LAdml6_pDY.mp4

3.5 MB

Part 10-Module 02-Lesson 05_Trees/19. Red-Black Trees - Insertion-dIuWLtWnkgs.mp4

3.5 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png

3.5 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/img/screen-shot-2016-11-24-at-12.09.24-pm.png

3.5 MB

Part 10-Module 01-Lesson 05_Interview Practice/01. Machine Learning Interview-y0yKRmgDKY4.mp4

3.5 MB

Part 05-Module 01-Lesson 01_Neural Networks/13. Error Functions-YfUUunxWIJw.mp4

3.5 MB

Part 03-Module 01-Lesson 06_Ensemble Methods/06. MLND SL EM 06 Weighting The Models MAIN V2-unCJ_ifVquU.mp4

3.6 MB

Part 10-Module 02-Lesson 06_Graphs/03. Directions and Cycles-lF0vUktQDPo.mp4

3.6 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/16. 17 L Transition Into Practical Aspects Of Learning-bKqkRFOOKoA.mp4

3.6 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/12. MLND - Unsupervised Learning - L2 09 DBSCAN Implementation MAIN V1 V1-qEMUzQFylg8.mp4

3.6 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/01. Introducing Luis-nto-stLuN6M.mp4

3.6 MB

Part 05-Module 01-Lesson 01_Neural Networks/10. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.7 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/08. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4

3.7 MB

Part 10-Module 02-Lesson 06_Graphs/11. BFS-pol4kGNlvJA.mp4

3.7 MB

Part 10-Module 02-Lesson 05_Trees/12. BSTs-abRNGLhGUmE.mp4

3.7 MB

Part 09-Module 02-Lesson 01_GitHub Review/03. Good GitHub repository-qBi8Q1EJdfQ.mp4

3.7 MB

Part 10-Module 02-Lesson 05_Trees/20. Tree Rotations-O5Yl-m0YbVA.mp4

3.7 MB

Part 05-Module 01-Lesson 01_Neural Networks/24. Gradient Descent-rhVIF-nigrY.mp4

3.8 MB

Part 10-Module 02-Lesson 04_Maps and Hashing/05. Hashing-kCPFfHx_LgQ.mp4

3.8 MB

Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 2-6nUUeQ9AeUA.mp4

3.8 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/04. Linear Boundaries-X-uMlsBi07k.mp4

3.8 MB

Part 05-Module 01-Lesson 01_Neural Networks/05. Linear Boundaries-X-uMlsBi07k.mp4

3.8 MB

Part 04-Module 04-Lesson 01_PCA/04. Slightly Less Perfect Data-9O7cJSP4C8w.mp4

3.8 MB

Part 03-Module 01-Lesson 01_Linear Regression/11. Minimizing Error Functions-RbT2TXN_6tY.mp4

3.8 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/13. MDPs, Part 1-NBWbluSbxPg.mp4

3.9 MB

Part 04-Module 04-Lesson 01_PCA/10. Practice Finding Centers-PRjmvj6Vubs.mp4

3.9 MB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-xJtmPbEfpFo.mp4

3.9 MB

Part 03-Module 01-Lesson 01_Linear Regression/01. Welcome To Linear Regression-zxZkTkM34BY.mp4

3.9 MB

Part 10-Module 02-Lesson 05_Trees/18. Self-Balancing Trees-EHI548K3jiw.mp4

3.9 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/21. 30 L Stochastic Gradient Descent-U9iEGUd9kJ0.mp4

3.9 MB

Part 10-Module 02-Lesson 06_Graphs/10. DFS-BC8jEidd2EQ.mp4

3.9 MB

Part 04-Module 04-Lesson 01_PCA/09. Second Principal Component Of New System-PqtW_Ux2_nY.mp4

4.0 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/18. Batch vs Stochastic Gradient Descent-2p58rVgqsgo.mp4

4.0 MB

Part 04-Module 03-Lesson 01_Feature Scaling/02. A Metric for Chris-O0bvLU4l0is.mp4

4.0 MB

Part 09-Module 02-Lesson 01_GitHub Review/06. Quick Fixes-Lb9e2KemR6I.mp4

4.0 MB

Part 03-Module 01-Lesson 08_Supervised Learning Project/01. ML Charity Project-aVodYHcOB8U.mp4

4.0 MB

Part 05-Module 01-Lesson 01_Neural Networks/16. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4

4.0 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/04. Decision Trees Answer-h8zH47iFhCo.mp4

4.0 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/01. Interview Introduction-dRsHYt1Lddc.mp4

4.1 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/18. Normalized Inputs And Initial Weights-WaHQ9-UXIIg.mp4

4.1 MB

Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-aveIz1JYeAg.mp4

4.1 MB

Part 10-Module 02-Lesson 05_Trees/03. Tree Terminology-mPUsDUR_sj8.mp4

4.1 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/06. Bellman Equations-UgIaDMvSdUo.mp4

4.1 MB

Part 05-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 03_Decision Trees/05. MLND SL DT 04 Q Student Admissions V3 MAIN V1-MOa335cQGI4.mp4

4.2 MB

Part 04-Module 04-Lesson 01_PCA/03. One-Dimensional, or Two-yhzQ_HJcwn8.mp4

4.2 MB

Part 03-Module 01-Lesson 03_Decision Trees/03. MLND SL DT 02 Recommending Apps 2 MAIN V3-KSrIYqKZwCA.mp4

4.2 MB

Part 10-Module 02-Lesson 04_Maps and Hashing/09. String Keys-WyFwieF1NN4.mp4

4.2 MB

Part 10-Module 02-Lesson 06_Graphs/07. Adjacency Matrices-FsFhoTALA1c.mp4

4.2 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/14. Dropout-Ty6K6YiGdBs.mp4

4.2 MB

Part 05-Module 01-Lesson 01_Neural Networks/20. Cross Entropy 1-iREoPUrpXvE.mp4

4.2 MB

Part 03-Module 01-Lesson 01_Linear Regression/08. Gradient Descent-4s4x9h6AN5Y.mp4

4.3 MB

Part 10-Module 02-Lesson 05_Trees/15. Heaps-M3B0UJWS_ag.mp4

4.3 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/13. TD Control Expected Sarsa-kEKupCyU0P0.mp4

4.3 MB

Part 03-Module 01-Lesson 03_Decision Trees/08. Entropy Formula-iZiSYrOKvpo.mp4

4.3 MB

Part 04-Module 02-Lesson 04_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 02_The RL Framework The Problem/06. The Reward Hypothesis-uAqNwgZ49JE.mp4

4.4 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/04. MLPs For Image Classification-TIFStebu530.mp4

4.4 MB

Part 10-Module 02-Lesson 05_Trees/05. Tree Traversal-KZOdmzypynw.mp4

4.5 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/10. Linear Regression Answer-L5QBqYDNJn0.mp4

4.5 MB

Part 04-Module 04-Lesson 01_PCA/27. PCA on the Enron Finance Data-w5XWkq_Y-rY.mp4

4.6 MB

Part 04-Module 02-Lesson 01_Clustering/06. Optimizing Centers (Rubber Bands)-nNR4hjhhGBc.mp4

4.6 MB

Part 10-Module 02-Lesson 05_Trees/09. Insert-j6PkPa2ZHWg.mp4

4.6 MB

Part 04-Module 02-Lesson 01_Clustering/15. Limitations of K-Means-4Fkfu37el_k.mp4

4.7 MB

Part 04-Module 04-Lesson 01_PCA/07. Center of a New Coordinate System-Kst3mlrqJnQ.mp4

4.7 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/04. Another Gridworld Example-n9SbomnLb-U.mp4

4.7 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/02. 02 Skin Cancer V4-70jGZeiTNgk.mp4

4.7 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/04. Combinando modelos-Boy3zHVrWB4.mp4

4.7 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/09. Notation Continued-ZeGnkrKZWBQ.mp4

4.7 MB

Part 03-Module 01-Lesson 03_Decision Trees/02. MLND SL DT 01 Recommending Apps 1 MAIN V3-uI_yNrqqKVg.mp4

4.8 MB

Part 05-Module 01-Lesson 01_Neural Networks/23. Error Function-V5kkHldUlVU.mp4

4.8 MB

Part 09-Module 02-Lesson 01_GitHub Review/16. Outro-dps7Ti6Lado.mp4

4.9 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/04. Knapsack Problem--xRKazHGtjU.mp4

4.9 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/01. Introduction-W2EP3riQSus.mp4

4.9 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/13. Non-Linear Function Approximation-rITnmpD2mN8.mp4

4.9 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/11. Model Complexity Graph-NnS0FJyVcDQ.mp4

5.0 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/01. M2L3 01 V1-YOSREyp04HA.mp4

5.0 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/18. Explore the Design Space-FG7M9tWH2nQ.mp4

5.0 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/10. Worst Case and Approximation-ZYcmui02J40.mp4

5.0 MB

Part 02-Module 02-Lesson 01_Evaluation Metrics/01. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4

5.0 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/24. Confusion Matrix-Question 1-9GLNjmMUB_4.mp4

5.0 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/15. SVM 13 RBF Kernel 2 V1-ozl9UWVP0MI.mp4

5.1 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

5.1 MB

Part 05-Module 01-Lesson 01_Neural Networks/07. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4

5.1 MB

Part 04-Module 02-Lesson 01_Clustering/08. Match Points (again)-5j6VZr8sHo8.mp4

5.1 MB

Part 03-Module 01-Lesson 04_Naive Bayes/11. MLND SL NB Naive Bayes Algorithm-CQBMB9jwcp8.mp4

5.1 MB

Part 06-Module 01-Lesson 01_Introduction to RL/01. Introduction-6jSFl5kxIBs.mp4

5.2 MB

Part 03-Module 01-Lesson 01_Linear Regression/06. Absolute Trick-DJWjBAqSkZw.mp4

5.2 MB

Part 04-Module 04-Lesson 01_PCA/13. When Does an Axis Dominate-5Uon6hUTl8Y.mp4

5.2 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/06. Model Validation in Keras-002jNXSM6CU.mp4

5.2 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/05. State-Value Functions-llakAjwox_8.mp4

5.3 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/05. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4

5.3 MB

Part 05-Module 01-Lesson 01_Neural Networks/15. Discrete vs. Continuous-Rm2KxFaPiJg.mp4

5.3 MB

Part 03-Module 01-Lesson 02_Perceptron Algorithm/01. Perception Algorithm V2-ebIlG6Pqwas.mp4

5.4 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/01. Introduction-9Wyf5Zsska8.mp4

5.4 MB

Part 02-Module 03-Lesson 01_Model Selection/02. Model Complexity Graph-Question-YS5OQCA5cLY.mp4

5.4 MB

Part 03-Module 01-Lesson 04_Naive Bayes/06. SL NB 05 Q False Positives V1 V2-ngA6v09eP08.mp4

5.4 MB

Part 03-Module 01-Lesson 03_Decision Trees/06. Student Admissions-TdgBi6LtOB8.mp4

5.4 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/05. Categorical Cross-Entropy-3sDYifgjFck.mp4

5.4 MB

Part 10-Module 02-Lesson 04_Maps and Hashing/06. Collisions-BUaWIjZ_ToY.mp4

5.4 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/19. 21 L Measuring Performance-byP0DJImOSk.mp4

5.5 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/12. Validating The Training-Oxm9ofvov3I.mp4

5.5 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/07. When do MLPs (not) work well-deMeuLdZN3Q.mp4

5.5 MB

Part 10-Module 02-Lesson 02_List-Based Collections/06. Linked Lists in Depth-ZONGA5wmREI.mp4

5.6 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/03. Dijkstra's Algorithm-SoPMK03cOgk.mp4

5.6 MB

Part 02-Module 01-Lesson 01_Training and Testing Models/09. Testing-gmxGRJSKEb0.mp4

5.6 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4

5.7 MB

Part 05-Module 01-Lesson 01_Neural Networks/18. Maximum Likelihood 1-1yJx-QtlvNI.mp4

5.7 MB

Part 04-Module 04-Lesson 01_PCA/01. Data Dimensionality-gg7SAMMl4kM.mp4

5.7 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/01. What Is Deep Learning-INt1nULYPak.mp4

5.8 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/15. Pooling Layers-OkkIZNs7Cyc.mp4

5.8 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/16. MLND - Unsupervised Learning - L3 17 Cluster Validation MAINv1 V1-N13ML_GUuZQ.mp4

5.9 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/04. SVM 03 Error Function V1-l-ahImxoi-U.mp4

5.9 MB

Part 10-Module 02-Lesson 06_Graphs/09. Graph Traversal-Dkt-XxHZaZE.mp4

6.0 MB

Part 10-Module 02-Lesson 06_Graphs/02. What Is a Graph-p-_DFOyEMV8.mp4

6.0 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/08. Optimality-j231aRV74QM.mp4

6.0 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/14. Efficiency of Quick Sort-aMb5GHPGQ1U.mp4

6.0 MB

Part 04-Module 02-Lesson 04_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 03-Lesson 01_Model Selection/05. Learning Curves SC V1-ZNhnNVKl8NM.mp4

6.0 MB

Part 04-Module 06-Lesson 01_Random Projection and ICA/04. L6 3 ICA V1 V1-ae94x-1JDzg.mp4

6.0 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/01. Case Study Introduction-r8uEDyBylHY.mp4

6.0 MB

Part 02-Module 02-Lesson 01_Evaluation Metrics/10. 08 F1 Score SC V1-TRzBeL07fSg.mp4

6.0 MB

Part 03-Module 01-Lesson 04_Naive Bayes/08. SL NB 07 Q Bayesian Learning 1 V1 V4-J4BmsKXPnkA.mp4

6.1 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/13. Quick Sort-kUon6854joI.mp4

6.1 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/03. RL M2L4 03 Two Function Approximators V1-37KQEgLaLfw.mp4

6.1 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/12. Logistic Regression Answer-JuAJd9Qvs6U.mp4

6.1 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/03. How Computers Interpret Images-V4f6p6uRhu8.mp4

6.2 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/01. Introduction-ek2PD9RDrWw.mp4

6.2 MB

Part 10-Module 02-Lesson 06_Graphs/01. Graph Introduction-DFR8F2Q9lgo.mp4

6.3 MB

Part 10-Module 02-Lesson 06_Graphs/06. Graph Representations-uw9u6dtl0WA.mp4

6.3 MB

Part 04-Module 04-Lesson 01_PCA/11. Practice Finding New Axes-aZqYc7v8BK4.mp4

6.3 MB

Part 03-Module 01-Lesson 03_Decision Trees/04. Recommending Apps-nEvW8B1HNq4.mp4

6.3 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/19. Kernel Method Answer-dRFd6HaAXys.mp4

6.4 MB

Part 04-Module 02-Lesson 04_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 10-Module 02-Lesson 02_List-Based Collections/03. Arrays-OnPP5xDmFv0.mp4

6.5 MB

Part 10-Module 02-Lesson 05_Trees/01. Trees-PXie7f22v2Q.mp4

6.5 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/06. Backpropagation V2-1SmY3TZTyUk.mp4

6.5 MB

Part 02-Module 03-Lesson 01_Model Selection/01. 04 L Types Of Errors-Twf1qnPZeSY.mp4

6.6 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/09. Action-Value Functions-KJLaRfOOPGA.mp4

6.6 MB

Part 05-Module 01-Lesson 01_Neural Networks/21. CrossEntropy V1-1BnhC6e0TFw.mp4

6.6 MB

Part 10-Module 02-Lesson 05_Trees/10. Binary Search Trees-7-ZQrugO-Yc.mp4

6.6 MB

Part 04-Module 03-Lesson 01_Feature Scaling/01. Chris's T-Shirt Size (Intuition)-oaqjLyiKOIA.mp4

6.6 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/18. ROC Curve-2Iw5TiGzJI4.mp4

6.7 MB

Part 02-Module 02-Lesson 01_Evaluation Metrics/12. ROC Curve-2Iw5TiGzJI4.mp4

6.7 MB

Part 04-Module 04-Lesson 01_PCA/12. Which Data is Ready for PCA-Su7kIUVPu6w.mp4

6.7 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/06. Runtime Analysis-8bI9OgOB2qI.mp4

6.7 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/07. Traveling Salesman Problem-9ruR5Ux63QU.mp4

6.8 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/01. Introduction-bYeteZQrUcE.mp4

6.8 MB

Part 04-Module 04-Lesson 01_PCA/19. Advantages of Maximal Variance-jQaYAlZ1fp0.mp4

6.8 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/14. MDPs, Part 2-CUTtQvxKkNw.mp4

6.8 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/07. Goals and Rewards, Part 1-XPnj3Ya3EuM.mp4

6.8 MB

Part 10-Module 01-Lesson 04_Land a Job Offer/01. Land a Job Offer-ZQJoT8QL_hw.mp4

6.8 MB

Part 04-Module 02-Lesson 01_Clustering/12. K-Means Clustering Visualization 3-WfwX3B4d8_I.mp4

6.9 MB

Part 09-Module 02-Lesson 01_GitHub Review/08. Writing READMEs with Walter-DQEfT2Zq5_o.mp4

6.9 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/09. Generalized Policy Iteration-XRmz4nolEsw.mp4

6.9 MB

Part 06-Module 01-Lesson 01_Introduction to RL/05. Resources-_YPqfAnCqtk.mp4

7.0 MB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-B_JKtLN-i5I.mp4

7.0 MB

Part 04-Module 02-Lesson 04_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 04-Module 04-Lesson 01_PCA/05. Trickiest Data Dimensionality-mTcuS5jUeUE.mp4

7.0 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/10. SVM 08 The C Parameter V2-6CxPhVo0hRw.mp4

7.0 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/11. SVM 09 Polynomial Kernel 1 V1-8t2tVDHNBnk.mp4

7.1 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/11. Optimal Policies-2rguYpVyCto.mp4

7.1 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/13. Wrap Up-x6JggcDTcys.mp4

7.2 MB

Part 05-Module 01-Lesson 01_Neural Networks/14. Error Functions-jfKShxGAbok.mp4

7.2 MB

Part 03-Module 01-Lesson 04_Naive Bayes/05. SL NB 04 Bayes Theorem V1 V2-nVbPJmf53AI.mp4

7.2 MB

Part 04-Module 02-Lesson 01_Clustering/17. Counterintuitive Clusters 2-HyjBus7S2gY.mp4

7.2 MB

Part 10-Module 02-Lesson 05_Trees/06. Depth-First Traversals-wp5ohHFTieM.mp4

7.3 MB

Part 04-Module 02-Lesson 01_Clustering/03. Clustering Movies-g8PKffm8IRY.mp4

7.3 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/02. The Setting, Revisited-V6Q1uF8a6kA.mp4

7.4 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/03. Logistic Regression - Solution-1iNylA3fJDs.mp4

7.4 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/06. Dynamic Programming-VQeFcG9pjJU.mp4

7.4 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/06. MLND - Unsupervised Learning - L2 06 Hierarchical Clustering Implementation MAIN V1 V1-tRqKsk5M9Mc.mp4

7.5 MB

Part 05-Module 01-Lesson 01_Neural Networks/02. Introduction-tn-CrUTkCUc.mp4

7.5 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/img/chess-game.jpg

7.5 MB

Part 05-Module 01-Lesson 03_Deep Neural Networks/13. Regularization-ndYnUrx8xvs.mp4

7.6 MB

Part 10-Module 02-Lesson 02_List-Based Collections/08. Stacks-DQoCO8aGcNc.mp4

7.6 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/07. TD Control Sarsa(0)-LkFkjfsRpXc.mp4

7.6 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/08. M2L3 08 V1-og3W6CXn1F0.mp4

7.6 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/08. Exact and Approximate Algorithms-3A8YqOYlAwQ.mp4

7.6 MB

Part 04-Module 04-Lesson 01_PCA/15. From Four Features to Two-MEtIAGKweXU.mp4

7.7 MB

Part 06-Module 01-Lesson 01_Introduction to RL/03. The Setting-nh8Gwdu19nc.mp4

7.8 MB

Part 10-Module 01-Lesson 03_Interview Fails/01. Interview Fails-FD6UNqMa0xc.mp4

7.8 MB

Part 04-Module 04-Lesson 01_PCA/06. PCA for Data Transformation-nDuo5ECT1G4.mp4

7.9 MB

Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-2HY0Yr5FRn0.mp4

7.9 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/07. Bubble Sort-h_osLG3GmjE.mp4

7.9 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/08. Efficiency of Bubble Sort-KddkHygi7is.mp4

7.9 MB

Part 04-Module 06-Lesson 01_Random Projection and ICA/05. L6 4 ICA Algorithm V2 V1-xlhd5UWk_-E.mp4

8.0 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/12. Stride and Padding-0r9o8hprDXQ.mp4

8.0 MB

Part 03-Module 01-Lesson 03_Decision Trees/10. Entropy Formula-w73JTBVeyjE.mp4

8.0 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/10. Convolutional Layers-h5R_JvdUrUI.mp4

8.0 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/08. Goals and Rewards, Part 2-pVIFc72VYH8.mp4

8.0 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4

8.1 MB

Part 04-Module 02-Lesson 01_Clustering/16. Counterintuitive Clusters-StmEUgT1XSY.mp4

8.1 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/17. Policy Iteration-gqv7o1kBDc0.mp4

8.1 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/03. Confirming Inputs-8lPTOG1yLsg.mp4

8.2 MB

Part 05-Module 01-Lesson 05_Deep Learning for Cancer Detection with Sebastian Thrun/26. Conclusion-WhpE_8sTt-0.mp4

8.2 MB

Part 10-Module 02-Lesson 04_Maps and Hashing/01. Introduction to Maps-JEw3iQAnGKQ.mp4

8.2 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/02. Purpose-7F7cMCTcyhM.mp4

8.2 MB

Part 11-Module 04-Lesson 01_Deep Neural Networks/08. Regularization Intro-pECnr-5F3_Q.mp4

8.3 MB

Part 04-Module 02-Lesson 04_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 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/03. MLND - Unsupervised Learning - L3 3 Gaussian Distribution In 1D MAINv1 V1-uDPFrZwsKKQ.mp4

8.4 MB

Part 11-Module 05-Lesson 01_Convolutional Neural Networks/04. Convolutional Networks-ISHGyvsT0QY.mp4

8.4 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/22. Hierarchical Clustering-1PldDT8AwMA.mp4

8.5 MB

Part 06-Module 01-Lesson 01_Introduction to RL/02. Applications-CV6B84mKRNM.mp4

8.5 MB

Part 11-Module 04-Lesson 01_Deep Neural Networks/01. Mat HS-9P7UPWFu8w8.mp4

8.5 MB

Part 03-Module 01-Lesson 04_Naive Bayes/02. SL NB 01 Guess The Person V1 V1-tAOAjI-7ins.mp4

8.5 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/02. RL M2L4 02 A Better Score Function V2-_HBJ3l10-OE.mp4

8.7 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4

8.7 MB

Part 03-Module 01-Lesson 01_Linear Regression/22. Regularization-PyFNIcsNma0.mp4

8.8 MB

Part 10-Module 01-Lesson 01_Ace Your Interview/01. Introduction-pg4HUMgKLxI.mp4

8.9 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/08. Notation Intro-xHwIU4j3gBc.mp4

8.9 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/12. Kernel Functions-RdkPVYyVOvU.mp4

8.9 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/06. Pitching to a Recruiter-LxAdWaA-qTQ.mp4

8.9 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/01. Intro to Deep Q-Learning-o3cmuUDhP3I.mp4

9.1 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/12. MC Control Policy Evaluation-3_opwMzpEEI.mp4

9.1 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/09. MLND - Unsupervised Learning - L2 07 HC Examples & Applications MAIN V1 V2-HTahFoQwk2g.mp4

9.2 MB

Part 11-Module 02-Lesson 01_Intro to TensorFlow/02. Solving Problems - Big And Small-WHcRQMGSbqg.mp4

9.2 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/23. Visualizing CNNs-mnqS_EhEZVg.mp4

9.2 MB

Part 03-Module 01-Lesson 03_Decision Trees/15. MLND SL DT 13 Random Forests MAIN V1-n5DhXhcYKcw.mp4

9.2 MB

Part 04-Module 06-Lesson 01_Random Projection and ICA/01. L6 1 Random Projection MAIN V1 V1 V1-Iat1a8mzI-Y.mp4

9.2 MB

Part 02-Module 03-Lesson 01_Model Selection/03. Model-Complexity-Graph Solution 2-5pWHGkNyRhA.mp4

9.2 MB

Part 03-Module 01-Lesson 03_Decision Trees/13. Information Gain-k9iZL53PAmw.mp4

9.2 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/04. Test Cases-7CNatJ7PqZ4.mp4

9.2 MB

Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-pMjG1IJRSb8.mp4

9.2 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/16. SVM 14 RBF Kernel 3 V1-DctkE8kaWPY.mp4

9.3 MB

Part 03-Module 01-Lesson 04_Naive Bayes/10. SL NB 09 Bayesian Learning 3 V1 V4-u-Hj4RsJn1o.mp4

9.3 MB

Part 10-Module 01-Lesson 05_Interview Practice/02. Mindset and Skills-OvjI0rveWnM.mp4

9.4 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/03. Cover Letter Components-DVvLiKedRw4.mp4

9.5 MB

Part 06-Module 01-Lesson 01_Introduction to RL/04. OpenAI Gym-MktEOWp3QLg.mp4

9.5 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4

9.5 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4

9.5 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/04. Describe Your Work Experiences-B1LED4txinI.mp4

9.5 MB

Part 09-Module 02-Lesson 01_GitHub Review/01. Introduction-Vnj2VNQROtI.mp4

9.6 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/12. SVM 10 Polynomial Kernel 2 V2-9RfFvZ9DIRg.mp4

9.7 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/06. TD Prediction Action Values-1c029-7_9GA.mp4

9.7 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/23. Conclusion-hJEuaOUu2yA.mp4

9.7 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/16. Neural Networks-xFu1_2K2D2U.mp4

9.8 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/07. Format-Xlqoq-SoJso.mp4

9.8 MB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/05. What Motivates You at the Workplace-Aa9SFwiRbho.mp4

9.8 MB

Part 04-Module 06-Lesson 01_Random Projection and ICA/10. L6 6 ICA Applications MAIN V1 V1 V1-th12mTv1B7g.mp4

9.9 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/14. Summary-MTEBk43oByU.mp4

9.9 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/10. Cumulative Reward-ysriH65lV9o.mp4

10 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/05. Elevator Pitch-0QtgTG49E9I.mp4

10 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/04. RL M2L4 04 The Actor And The Critic V1-bvbE9F7urd4.mp4

10 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/03. Episodic vs. Continuing Tasks-E1I-BPanSM8.mp4

10 MB

Part 01-Module 02-Lesson 01_Career Services Available to You/01. Meet the Careers Team-cuKecPpZ7PM.mp4

10 MB

Part 10-Module 02-Lesson 07_Case Studies in Algorithms/05. A Faster Algorithm-J7S3CHFBZJA.mp4

10 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/04. MLND - Unsupervised Learning - L3 04 GMM Clustering In 1D MAIN V1 V1-JkRQIGqkqA4.mp4

10 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/20. Image Augmentation in Keras-odStujZq3GY.mp4

10 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/09. Coarse Coding-Uu1J5KLAfTU.mp4

10 MB

Part 04-Module 04-Lesson 01_PCA/21. Info Loss and Principal Components-LTPV8lxQeZQ.mp4

10 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/01. RL M2L4 01 Actor Critic Methods RENDER V1 V1-FXhyxJzgt8U.mp4

10 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/03. Monte Carlo Learning-qOviWYwcvsg.mp4

10 MB

Part 08-Module 01-Lesson 01_Conduct a Job Search/01. Introduction-axcFtHK6If4.mp4

10 MB

Part 04-Module 02-Lesson 01_Clustering/11. K-Means Clustering Visualization 2-fQXXa-CAoS0.mp4

10 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/05. RL M2L4 05 Advantage Function RENDER V1 V2-vpLmzKqcgfc.mp4

11 MB

Part 04-Module 04-Lesson 01_PCA/30. PCA for Facial Recognition-WyoU2otqsd8.mp4

11 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/07. Tile Coding-BRs7AnTZ_8k.mp4

11 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/11. Gradient Descent-Math-7sxA5Ap8AWM.mp4

11 MB

Part 04-Module 03-Lesson 01_Feature Scaling/06. Comparing Features with Different Scales-PRL8trOU7Rs.mp4

12 MB

Part 04-Module 04-Lesson 01_PCA/18. Maximal Variance-tfYAGBIR_Ws.mp4

12 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4

12 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4

12 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/01. Convey Your Skills Concisely-xnQr3ohml9s.mp4

12 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/15. MLND - Unsupervised Learning - L3 16 Cluster Analysis Process MAIN V1 V1-aI2wW4fcU1I.mp4

12 MB

Part 10-Module 02-Lesson 02_List-Based Collections/01. Welcome to Collections-cZORvZq-tI0.mp4

12 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/02. Efficiency of Binary Search-7WbRB7dSyvc.mp4

12 MB

Part 08-Module 01-Lesson 01_Conduct a Job Search/02. Job Search Mindset-cBk7bno3KS0.mp4

12 MB

Part 04-Module 03-Lesson 01_Feature Scaling/12. Quiz on Algorithms Requiring Rescaling-oEhevl5DWpk.mp4

12 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/03. Resume Structure-POM0MqLTj98.mp4

12 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/03. Resume Structure-POM0MqLTj98.mp4

12 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/03. Resume Structure-POM0MqLTj98.mp4

12 MB

Part 10-Module 02-Lesson 04_Maps and Hashing/04. Introduction to Hashing-8yik3RlDFgM.mp4

12 MB

Part 03-Module 01-Lesson 03_Decision Trees/09. MLND SL DT 08 Entropy Formula 2 MAIN V2-6GHg70hrSJw.mp4

12 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/18. MC Control Constant-alpha-QFV1nI9Zpoo.mp4

12 MB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/07. What Do You Know About the Company-CcTfHemUvbM.mp4

12 MB

Part 04-Module 04-Lesson 01_PCA/16. Compression While Preserving Information-NjuenhkC-44.mp4

12 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/05. Discretization-j2eZyUpy--E.mp4

12 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/06. SVM 05 Classification Error V1-nWGVAGXwvGE.mp4

13 MB

Part 03-Module 01-Lesson 03_Decision Trees/07. Entropy-piLpj1V1HEk.mp4

13 MB

Part 04-Module 04-Lesson 01_PCA/25. ReviewDefinition of PCA-oFBGXUUuKyI.mp4

13 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/05. Resume Reflection-8Cj_tCp8mls.mp4

13 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/05. Resume Reflection-8Cj_tCp8mls.mp4

13 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/05. Resume Reflection-8Cj_tCp8mls.mp4

13 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/02. Neural Nets as Value Functions-cBi7vLrk8QQ.mp4

13 MB

Part 04-Module 02-Lesson 01_Clustering/02. Unsupervised Learning-Mx9f99bRB3Q.mp4

13 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/06. Resume Review-L3F2BFGYMtI.mp4

13 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/06. Resume Review-L3F2BFGYMtI.mp4

13 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/06. Resume Review-L3F2BFGYMtI.mp4

13 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/26. Transfer Learning in Keras-HsIAznMM1LA.mp4

13 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/05. SVM 04 Perceptron Algorithm V1-IIlQHBOrD6Q.mp4

13 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/09. Local Connectivity-z9wiDg0w-Dc.mp4

13 MB

Part 03-Module 01-Lesson 03_Decision Trees/14. Maximizing Information Gain-3FgJOpKfdY8.mp4

13 MB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/03. Program Structure-rjk8-r-Aa5U.mp4

13 MB

Part 09-Module 02-Lesson 01_GitHub Review/09. Interview with Art - Part 2-Vvzl2J5K7-Y.mp4

13 MB

Part 01-Module 01-Lesson 02_What is Machine Learning/21. K-means Clustering-pv_i08zjpQw.mp4

13 MB

Part 08-Module 02-Lesson 02_Refine Your Career Change Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4

13 MB

Part 08-Module 02-Lesson 01_Refine Your Entry-Level Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4

13 MB

Part 08-Module 02-Lesson 03_Refine Your Prior Industry Experience Resume/02. Effective Resume Components-AiFcaHRGdEA.mp4

13 MB

Part 04-Module 04-Lesson 01_PCA/20. Maximal Variance and Information Loss-hfmvk8DzTGA.mp4

13 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/25. Transfer Learning-LHG5FltaR6I.mp4

13 MB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/01. 01 MLNDIntro Program Welcome V3-A8AnsR6e75I.mp4

13 MB

Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-lKAZqlhLBxc.mp4

14 MB

Part 01-Module 01-Lesson 01_Welcome to Machine Learning/02. Projects You Will Build-P7YK47GUGWk.mp4

14 MB

Part 10-Module 01-Lesson 03_Interview Fails/02. Interviewing Fails Mike Wales-OGXRmzBglI4.mp4

14 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/20. Truncated Policy Iteration-a-RvCxlPMho.mp4

14 MB

Part 10-Module 02-Lesson 02_List-Based Collections/05. Linked Lists-zxkpZrozDUk.mp4

14 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/11. Discounted Return-opXGNPwwn7g.mp4

14 MB

Part 03-Module 01-Lesson 04_Naive Bayes/07. SL NB 06 S False Positives V1 V3-Bg6_Tvcv81A.mp4

14 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/04. Writing Your Introduction-5S5PH73WLLY.mp4

14 MB

Part 10-Module 02-Lesson 02_List-Based Collections/09. Stacks Details-HpaVHzDeZC4.mp4

14 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/10. Merge Sort-K916wfSzKxE.mp4

15 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/01. Binary Search-0VN5iwEyq4c.mp4

15 MB

Part 06-Module 01-Lesson 02_The RL Framework The Problem/17. MDPs, Part 3-UlXHFbla3QI.mp4

15 MB

Part 04-Module 03-Lesson 01_Feature Scaling/11. MinMax Scaler in sklearn-lgoh5R05YM0.mp4

15 MB

Part 11-Module 03-Lesson 01_Intro to Neural Networks/04. Neural Networks-Mqogpnp1lrU.mp4

15 MB

Part 10-Module 01-Lesson 01_Ace Your Interview/02. Interviewing Conversations-klqXp09Pen4.mp4

15 MB

Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-sbB-0qV33uM.mp4

15 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/01. Course Introduction-NKBUbUiedzc.mp4

15 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/01. MLND - Unsupervised Learning - L2 01 V2-NHb8w_M8nDY.mp4

16 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/23. Value Iteration-XNeQn8N36y8.mp4

16 MB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/04. Time When You Showed Initiative-29mkriaGT0E.mp4

16 MB

Part 10-Module 01-Lesson 05_Interview Practice/10. Conclusion-mnQ2n026Y2o.mp4

16 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/11. Efficiency of Merge Sort-HKiK5Y-YSkk.mp4

16 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/10. TD Control Sarsamax-4DxoYuR7aZ4.mp4

16 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/07. Use Your Elevator Pitch-e-v60ieggSs.mp4

17 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/05. M2L3 05 V1-eZxxNNIZuwA.mp4

17 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/01. Get an Interview with a Cover Letter!-BH1KY63YfAM.mp4

17 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/02. Clarifying the Question-XvvKBmKC_84.mp4

17 MB

Part 06-Module 02-Lesson 04_Actor-Critic Methods/07. Summary-hvYQ_3LgCYs.mp4

17 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/04. Temporal Difference Learning-lpmDi0QeUm8.mp4

17 MB

Part 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-r7g0Z-54vg0.mp4

17 MB

Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-sunl9foctXg.mp4

17 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/05. Q-Learning-AI5gLgYMSq8.mp4

17 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/01. Why Network-exjEm9Paszk.mp4

17 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/09. Deep Q-Learning Algorithm-MqTXoCxQ_eY.mp4

17 MB

Part 04-Module 04-Lesson 01_PCA/29. When to Use PCA-hJZHcmJBk1o.mp4

18 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/02. Applications of CNNs-HrYNL_1SV2Y.mp4

18 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/15. MLND - Unsupervised Learning - L2 10 DBSCAN Examples & Applications MAIN V1 V2-GhyFsjQ4FkA.mp4

18 MB

Part 04-Module 04-Lesson 01_PCA/17. Composite Features-spVqFnSvlIU.mp4

18 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/03. MLND - Unsupervised Learning - L2 03 V2-pd9Ix3WMP_Q.mp4

18 MB

Part 05-Module 01-Lesson 04_Convolutional Neural Networks/17. CNNs For Image Classification-l9vg_1YUlzg.mp4

18 MB

Part 08-Module 03-Lesson 01_Craft Your Cover Letter/05. Writing the Body-aK9Qnv3a6Wg.mp4

18 MB

Part 10-Module 01-Lesson 03_Interview Fails/03. Interviewing Fails Siya Raj Purohit-wYop-N5YgeA.mp4

18 MB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/08. Time When You Dealt With Failure-Qb4o_4hCuyg.mp4

18 MB

Part 10-Module 02-Lesson 01_Introduction and Efficiency/07. Efficiency-I-RASDPbDrI.mp4

18 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/14. SVM 12 RBF Kernel 1 V3-xdkIulxXWfQ.mp4

19 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/07. SVM 06 Margin Error V2-dSac8Gfgbok.mp4

19 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/03. M2L3 03 V2-TePX-0Bs23E.mp4

19 MB

Part 04-Module 02-Lesson 04_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 05-Module 01-Lesson 04_Convolutional Neural Networks/11. Convolutional Layers-RnM1D-XI--8.mp4

20 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/06. Intro to Sorting-Z6yuIen71zM.mp4

20 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/11. MLND - Unsupervised Learning - L2 08 DBSCAN MAIN V1 V2--dqyFkfnctI.mp4

20 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/10. MC Control Incremental Mean-E2RITH-2NUE.mp4

20 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/10. Function Approximation-UTGWVY6jEdg.mp4

20 MB

Part 06-Module 01-Lesson 03_The RL Framework The Solution/02. Policies-hc3LrvaC13U.mp4

20 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/02. Elevator Pitch-S-nAHPrkQrQ.mp4

21 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/01. Introduction-yXErXQulI_o.mp4

21 MB

Part 10-Module 02-Lesson 02_List-Based Collections/11. Queues-XAbzlilAHZw.mp4

21 MB

Part 04-Module 04-Lesson 01_PCA/23. PCA for Feature Transformation-8kUPRUEMCA8.mp4

21 MB

Part 04-Module 04-Lesson 01_PCA/28. PCA in sklearn-SBYdqlLgbGk.mp4

21 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/08. Fixed Q Targets-SWpyiEezfp4.mp4

21 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/04. M2L3 04 V1-QicxmyE5vTo.mp4

21 MB

Part 03-Module 01-Lesson 04_Naive Bayes/04. SL NB 03 Guess The Person Now V1 V2-pQgO1KF90yU.mp4

21 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/03. Discrete vs. Continuous Spaces-uHstLeRzaE8.mp4

21 MB

Part 03-Module 01-Lesson 03_Decision Trees/01. MLND SL DT 00 Intro V2-l34ijtQhVNk.mp4

22 MB

Part 09-Module 02-Lesson 01_GitHub Review/04. Interview with Art - Part 1-ClLYamtaO-Q.mp4

22 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/13. MC Control Policy Improvement-2RKH-BInX7s.mp4

22 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/06. MC Prediction Action Values-08tLtbh0xLs.mp4

22 MB

Part 08-Module 01-Lesson 01_Conduct a Job Search/03. Target Your Application to An Employer-X9JBzbrkcvs.mp4

22 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/05. MLND - Unsupervised Learning - L2 05 CompleteLink AverageLink Ward MAIN V1 V2-dWGQVcZ95d0.mp4

22 MB

Part 04-Module 02-Lesson 04_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 02-Lesson 01_Clustering/13. Sklearn-3zHUAXcoZ7c.mp4

23 MB

Part 04-Module 02-Lesson 03_Hierarchical and Density-based Clustering/04. MLND - Unsupervised Learning - L2 04 Examining SingleLink Clustering MAIN V1 V2-foLcmCOLDos.mp4

23 MB

Part 10-Module 02-Lesson 03_Searching and Sorting/04. Recursion-_aI2Jch6Epk.mp4

25 MB

Part 09-Module 02-Lesson 01_GitHub Review/13. Interview with Art - Part 3-M6PKr3S1rPg.mp4

25 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/06. Deep Q Network-GgtR_d1OB-M.mp4

26 MB

Part 04-Module 02-Lesson 04_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 06-Module 01-Lesson 04_Dynamic Programming/08. Iterative Policy Evaluation-eDXIL_oOJHI.mp4

27 MB

Part 03-Module 01-Lesson 05_Support Vector Machines/13. SVM 11 Polynomial Kernel 3 V1-XmbK8OjbX5U.mp4

27 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/05. An Iterative Method-AX-hG3KvwzY.mp4

28 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/11. Linear Function Approximation-OJ5wrB7o-pI.mp4

29 MB

Part 06-Module 01-Lesson 06_Temporal-Difference Methods/03. TD Prediction TD(0)-CsD6b0csU7o.mp4

30 MB

Part 06-Module 01-Lesson 04_Dynamic Programming/14. Policy Improvement-4_adUEK0IHg.mp4

30 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/09. Debugging-Bz1tlvkql9Q.mp4

31 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/14. MLND - Unsupervised Learning - L3 15 GMM Examples And Applications MAIN V2 V1-FRoxeLp81Bg.mp4

32 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/05. Brainstorming-LJFYhMDCCsU.mp4

32 MB

Part 04-Module 04-Lesson 01_PCA/31. Eigenfaces Code-LgLYw-G4sLQ.mp4

32 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/02. M2L3 02 V2-ToS8vXGdODE.mp4

32 MB

Part 09-Module 01-Lesson 01_Develop Your Personal Brand/04. Meet Chris-0ccflD9x5WU.mp4

32 MB

Part 04-Module 02-Lesson 04_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 10-Module 01-Lesson 05_Interview Practice/08. Q5 - Describe Your ML Project-jjdbGD4CBGk.mp4

33 MB

Part 06-Module 02-Lesson 01_RL in Continuous Spaces/01. Deep Reinforcement Learning-GPjK124RU5g.mp4

33 MB

Part 06-Module 01-Lesson 05_Monte Carlo Methods/03. MC Prediction State Values-0q2wSWyuBj8.mp4

33 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/10. DQN Improvements-Zfdbp93A2GU.mp4

39 MB

Part 10-Module 01-Lesson 02_Practice Behavioral Questions/06. A Problem and How You Dealt With It-7IKqdW30GvQ.mp4

41 MB

Part 04-Module 02-Lesson 04_Gaussian Mixture Models and Cluster Validation/19. MLND - Unsupervised Learning - L3 20 Internal Validation Indices MAIN V1 V2-39JruOTptKI.mp4

41 MB

Part 06-Module 02-Lesson 03_Policy-Based Methods/07. M2L3 07 V2-ZBLLGIN1EfU.mp4

44 MB

Part 10-Module 01-Lesson 05_Interview Practice/06. Q3 - Detect Plagiarism-B3w_msqHP68.mp4

44 MB

Part 06-Module 02-Lesson 02_Deep Q-Learning/07. Experience Replay-wX_-SZG-YMQ.mp4

48 MB

Part 10-Module 01-Lesson 05_Interview Practice/09. Q6 - Explain How SVMs Work-RyThtU8GcT0.mp4

49 MB

Part 10-Module 01-Lesson 03_Interview Fails/04. Interviewing Fails Lyla Fujiwara-CgK2HxdJzc8.mp4

50 MB

Part 10-Module 01-Lesson 05_Interview Practice/07. Q4 - Reduce Data Dimensionality-NzzpasA9GsM.mp4

64 MB

Part 10-Module 01-Lesson 05_Interview Practice/04. Q1 - Predict Rain-ooqFCXMdxys.mp4

69 MB

Part 10-Module 01-Lesson 05_Interview Practice/05. Q2 - Identify Fish-bXpONCq5ePE.mp4

74 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/08. Coding 2-qEteyPNRSwU.mp4

105 MB

Part 10-Module 02-Lesson 08_Technical Interview - Python/07. Coding-zhQYREUI8Z0.mp4

105 MB