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Udemy - Master statistics & machine learning intuition, math, code (3.2025)

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File Name
Size
01. Introductions/1. Important Getting the most out of this course.mp4
45 MB
01. Introductions/1. Important Getting the most out of this course.vtt
6.4 kB
01. Introductions/2. About using MATLAB or Python.mp4
12 MB
01. Introductions/2. About using MATLAB or Python.vtt
5.8 kB
01. Introductions/3. Statistics guessing game!.mp4
48 MB
01. Introductions/3. Statistics guessing game!.vtt
13 kB
01. Introductions/3. stats_intro_GuessTheTest.zip
3.7 kB
01. Introductions/4. Using the Q&A forum.mp4
24 MB
01. Introductions/4. Using the Q&A forum.vtt
8.0 kB
01. Introductions/5. (optional) Entering time-stamped notes in the Udemy video player.mp4
7.1 MB
01. Introductions/5. (optional) Entering time-stamped notes in the Udemy video player.vtt
3.0 kB
02. Math prerequisites/1. Should you memorize statistical formulas.mp4
28 MB
02. Math prerequisites/1. Should you memorize statistical formulas.vtt
4.3 kB
02. Math prerequisites/2. Arithmetic and exponents.mp4
7.6 MB
02. Math prerequisites/2. Arithmetic and exponents.vtt
5.6 kB
02. Math prerequisites/3. Scientific notation.mp4
13 MB
02. Math prerequisites/3. Scientific notation.vtt
7.9 kB
02. Math prerequisites/4. Summation notation.mp4
7.7 MB
02. Math prerequisites/4. Summation notation.vtt
5.8 kB
02. Math prerequisites/5. Absolute value.mp4
6.9 MB
02. Math prerequisites/5. Absolute value.vtt
4.2 kB
02. Math prerequisites/6. Natural exponent and logarithm.mp4
18 MB
02. Math prerequisites/6. Natural exponent and logarithm.vtt
11 kB
02. Math prerequisites/7. The logistic function.mp4
18 MB
02. Math prerequisites/7. The logistic function.vtt
12 kB
02. Math prerequisites/8. Rank and tied-rank.mp4
14 MB
02. Math prerequisites/8. Rank and tied-rank.vtt
8.9 kB
03. IMPORTANT Download course materials/1. Download materials for the entire course!.mp4
51 MB
03. IMPORTANT Download course materials/1. Download materials for the entire course!.vtt
7.1 kB
03. IMPORTANT Download course materials/1. Link-to-code-on-github.txt
47 B
03. IMPORTANT Download course materials/1. Statistics_course-main.zip
2.5 MB
04. What are (is) data/1. Is data singular or plural!!!!.mp4
11 MB
04. What are (is) data/1. Is data singular or plural!!!!.vtt
2.4 kB
04. What are (is) data/2. Where do data come from and what do they mean.mp4
36 MB
04. What are (is) data/2. Where do data come from and what do they mean.vtt
8.2 kB
04. What are (is) data/3. Types of data categorical, numerical, etc.mp4
59 MB
04. What are (is) data/3. Types of data categorical, numerical, etc.vtt
20 kB
04. What are (is) data/4. Code representing types of data on computers.mp4
48 MB
04. What are (is) data/4. Code representing types of data on computers.vtt
12 kB
04. What are (is) data/5. Sample vs. population data.mp4
37 MB
04. What are (is) data/5. Sample vs. population data.vtt
17 kB
04. What are (is) data/6. Samples, case reports, and anecdotes.mp4
18 MB
04. What are (is) data/6. Samples, case reports, and anecdotes.vtt
7.8 kB
04. What are (is) data/7. The ethics of making up data.mp4
20 MB
04. What are (is) data/7. The ethics of making up data.vtt
10 kB
05. Visualizing data/1. Bar plots.mp4
37 MB
05. Visualizing data/1. Bar plots.vtt
15 kB
05. Visualizing data/10. Code pie charts.mp4
79 MB
05. Visualizing data/10. Code pie charts.vtt
19 kB
05. Visualizing data/11. When to use lines instead of bars.mp4
18 MB
05. Visualizing data/11. When to use lines instead of bars.vtt
8.3 kB
05. Visualizing data/12. Linear vs. logarithmic axis scaling.mp4
26 MB
05. Visualizing data/12. Linear vs. logarithmic axis scaling.vtt
12 kB
05. Visualizing data/13. Code line plots.mp4
37 MB
05. Visualizing data/13. Code line plots.vtt
11 kB
05. Visualizing data/14. Unsupervised learning log-scaled plots.mp4
3.7 MB
05. Visualizing data/14. Unsupervised learning log-scaled plots.vtt
2.4 kB
05. Visualizing data/2. Code bar plots.mp4
100 MB
05. Visualizing data/2. Code bar plots.vtt
23 kB
05. Visualizing data/3. Box-and-whisker plots.mp4
11 MB
05. Visualizing data/3. Box-and-whisker plots.vtt
7.7 kB
05. Visualizing data/4. Code box plots.mp4
84 MB
05. Visualizing data/4. Code box plots.vtt
12 kB
05. Visualizing data/5. Unsupervised learning Boxplots of normal and uniform noise.mp4
8.2 MB
05. Visualizing data/5. Unsupervised learning Boxplots of normal and uniform noise.vtt
3.7 kB
05. Visualizing data/6. Histograms.mp4
44 MB
05. Visualizing data/6. Histograms.vtt
16 kB
05. Visualizing data/7. Code histograms.mp4
134 MB
05. Visualizing data/7. Code histograms.vtt
23 kB
05. Visualizing data/8. Unsupervised learning Histogram proportion.mp4
12 MB
05. Visualizing data/8. Unsupervised learning Histogram proportion.vtt
3.4 kB
05. Visualizing data/9. Pie charts.mp4
16 MB
05. Visualizing data/9. Pie charts.vtt
8.0 kB
06. Descriptive statistics/1. Descriptive vs. inferential statistics.mp4
22 MB
06. Descriptive statistics/1. Descriptive vs. inferential statistics.vtt
6.2 kB
06. Descriptive statistics/10. Unsupervised learning central tendencies with outliers.mp4
17 MB
06. Descriptive statistics/10. Unsupervised learning central tendencies with outliers.vtt
4.3 kB
06. Descriptive statistics/11. Measures of dispersion (variance, standard deviation).mp4
57 MB
06. Descriptive statistics/11. Measures of dispersion (variance, standard deviation).vtt
26 kB
06. Descriptive statistics/12. Code Computing dispersion.mp4
266 MB
06. Descriptive statistics/12. Code Computing dispersion.vtt
36 kB
06. Descriptive statistics/13. Interquartile range (IQR).mp4
9.8 MB
06. Descriptive statistics/13. Interquartile range (IQR).vtt
6.7 kB
06. Descriptive statistics/14. Code IQR.mp4
83 MB
06. Descriptive statistics/14. Code IQR.vtt
22 kB
06. Descriptive statistics/15. QQ plots.mp4
16 MB
06. Descriptive statistics/15. QQ plots.vtt
10 kB
06. Descriptive statistics/16. Code QQ plots.mp4
90 MB
06. Descriptive statistics/16. Code QQ plots.vtt
22 kB
06. Descriptive statistics/17. Statistical moments.mp4
22 MB
06. Descriptive statistics/17. Statistical moments.vtt
12 kB
06. Descriptive statistics/18. Histograms part 2 Number of bins.mp4
24 MB
06. Descriptive statistics/18. Histograms part 2 Number of bins.vtt
14 kB
06. Descriptive statistics/19. Code Histogram bins.mp4
118 MB
06. Descriptive statistics/19. Code Histogram bins.vtt
18 kB
06. Descriptive statistics/2. Accuracy, precision, resolution.mp4
25 MB
06. Descriptive statistics/2. Accuracy, precision, resolution.vtt
11 kB
06. Descriptive statistics/20. Violin plots.mp4
6.5 MB
06. Descriptive statistics/20. Violin plots.vtt
4.7 kB
06. Descriptive statistics/21. Code violin plots.mp4
105 MB
06. Descriptive statistics/21. Code violin plots.vtt
14 kB
06. Descriptive statistics/22. Unsupervised learning asymmetric violin plots.mp4
17 MB
06. Descriptive statistics/22. Unsupervised learning asymmetric violin plots.vtt
3.6 kB
06. Descriptive statistics/23. Shannon entropy.mp4
33 MB
06. Descriptive statistics/23. Shannon entropy.vtt
15 kB
06. Descriptive statistics/24. Code entropy.mp4
97 MB
06. Descriptive statistics/24. Code entropy.vtt
28 kB
06. Descriptive statistics/25. Unsupervised learning entropy and number of bins.mp4
8.3 MB
06. Descriptive statistics/25. Unsupervised learning entropy and number of bins.vtt
2.0 kB
06. Descriptive statistics/3. Data distributions.mp4
32 MB
06. Descriptive statistics/3. Data distributions.vtt
16 kB
06. Descriptive statistics/4. Code data from different distributions.mp4
303 MB
06. Descriptive statistics/4. Code data from different distributions.vtt
44 kB
06. Descriptive statistics/5. Unsupervised learning histograms of distributions.mp4
10 MB
06. Descriptive statistics/5. Unsupervised learning histograms of distributions.vtt
2.8 kB
06. Descriptive statistics/6. The beauty and simplicity of Normal.mp4
10 MB
06. Descriptive statistics/6. The beauty and simplicity of Normal.vtt
8.0 kB
06. Descriptive statistics/7. Measures of central tendency (mean).mp4
39 MB
06. Descriptive statistics/7. Measures of central tendency (mean).vtt
18 kB
06. Descriptive statistics/8. Measures of central tendency (median, mode).mp4
34 MB
06. Descriptive statistics/8. Measures of central tendency (median, mode).vtt
18 kB
06. Descriptive statistics/9. Code computing central tendency.mp4
67 MB
06. Descriptive statistics/9. Code computing central tendency.vtt
19 kB
07. Data normalizations and outliers/1. Garbage in, garbage out (GIGO).mp4
12 MB
07. Data normalizations and outliers/1. Garbage in, garbage out (GIGO).vtt
5.8 kB
07. Data normalizations and outliers/10. Code z-score for outlier removal.mp4
137 MB
07. Data normalizations and outliers/10. Code z-score for outlier removal.vtt
32 kB
07. Data normalizations and outliers/11. Unsupervised learning z vs. modified-z.mp4
9.0 MB
07. Data normalizations and outliers/11. Unsupervised learning z vs. modified-z.vtt
3.9 kB
07. Data normalizations and outliers/12. Multivariate outlier detection.mp4
25 MB
07. Data normalizations and outliers/12. Multivariate outlier detection.vtt
13 kB
07. Data normalizations and outliers/13. Code Euclidean distance for outlier removal.mp4
44 MB
07. Data normalizations and outliers/13. Code Euclidean distance for outlier removal.vtt
12 kB
07. Data normalizations and outliers/14. Removing outliers by data trimming.mp4
17 MB
07. Data normalizations and outliers/14. Removing outliers by data trimming.vtt
8.3 kB
07. Data normalizations and outliers/15. Code Data trimming to remove outliers.mp4
65 MB
07. Data normalizations and outliers/15. Code Data trimming to remove outliers.vtt
15 kB
07. Data normalizations and outliers/16. Non-parametric solutions to outliers.mp4
23 MB
07. Data normalizations and outliers/16. Non-parametric solutions to outliers.vtt
6.4 kB
07. Data normalizations and outliers/17. Nonlinear data transformations.mp4
34 MB
07. Data normalizations and outliers/17. Nonlinear data transformations.vtt
20 kB
07. Data normalizations and outliers/18. An outlier lecture on personal accountability.mp4
18 MB
07. Data normalizations and outliers/18. An outlier lecture on personal accountability.vtt
4.3 kB
07. Data normalizations and outliers/2. Z-score standardization.mp4
36 MB
07. Data normalizations and outliers/2. Z-score standardization.vtt
14 kB
07. Data normalizations and outliers/3. Code z-score.mp4
67 MB
07. Data normalizations and outliers/3. Code z-score.vtt
18 kB
07. Data normalizations and outliers/4. Min-max scaling.mp4
12 MB
07. Data normalizations and outliers/4. Min-max scaling.vtt
7.1 kB
07. Data normalizations and outliers/5. Code min-max scaling.mp4
40 MB
07. Data normalizations and outliers/5. Code min-max scaling.vtt
11 kB
07. Data normalizations and outliers/6. Unsupervised learning Invert the min-max scaling.mp4
6.8 MB
07. Data normalizations and outliers/6. Unsupervised learning Invert the min-max scaling.vtt
3.7 kB
07. Data normalizations and outliers/7. What are outliers and why are they dangerous.mp4
43 MB
07. Data normalizations and outliers/7. What are outliers and why are they dangerous.vtt
21 kB
07. Data normalizations and outliers/8. Removing outliers z-score method.mp4
34 MB
07. Data normalizations and outliers/8. Removing outliers z-score method.vtt
14 kB
07. Data normalizations and outliers/9. The modified z-score method.mp4
9.6 MB
07. Data normalizations and outliers/9. The modified z-score method.vtt
5.8 kB
08. Probability theory/1. What is probability.mp4
41 MB
08. Probability theory/1. What is probability.vtt
17 kB
08. Probability theory/10. Code cdfs and pdfs.mp4
96 MB
08. Probability theory/10. Code cdfs and pdfs.vtt
14 kB
08. Probability theory/11. Unsupervised learning cdf's for various distributions.mp4
9.3 MB
08. Probability theory/11. Unsupervised learning cdf's for various distributions.vtt
3.4 kB
08. Probability theory/12. Creating sample estimate distributions.mp4
125 MB
08. Probability theory/12. Creating sample estimate distributions.vtt
27 kB
08. Probability theory/13. Monte Carlo sampling.mp4
8.8 MB
08. Probability theory/13. Monte Carlo sampling.vtt
3.9 kB
08. Probability theory/14. Sampling variability, noise, and other annoyances.mp4
106 MB
08. Probability theory/14. Sampling variability, noise, and other annoyances.vtt
13 kB
08. Probability theory/15. Code sampling variability.mp4
155 MB
08. Probability theory/15. Code sampling variability.vtt
36 kB
08. Probability theory/16. Expected value.mp4
60 MB
08. Probability theory/16. Expected value.vtt
15 kB
08. Probability theory/17. Conditional probability.mp4
86 MB
08. Probability theory/17. Conditional probability.vtt
18 kB
08. Probability theory/18. Code conditional probabilities.mp4
115 MB
08. Probability theory/18. Code conditional probabilities.vtt
28 kB
08. Probability theory/19. Tree diagrams for conditional probabilities.mp4
14 MB
08. Probability theory/19. Tree diagrams for conditional probabilities.vtt
9.4 kB
08. Probability theory/2. Probability vs. proportion.mp4
38 MB
08. Probability theory/2. Probability vs. proportion.vtt
13 kB
08. Probability theory/20. The Law of Large Numbers.mp4
41 MB
08. Probability theory/20. The Law of Large Numbers.vtt
14 kB
08. Probability theory/21. Code Law of Large Numbers in action.mp4
166 MB
08. Probability theory/21. Code Law of Large Numbers in action.vtt
26 kB
08. Probability theory/22. The Central Limit Theorem.mp4
27 MB
08. Probability theory/22. The Central Limit Theorem.vtt
16 kB
08. Probability theory/23. Code the CLT in action.mp4
93 MB
08. Probability theory/23. Code the CLT in action.vtt
23 kB
08. Probability theory/24. Unsupervised learning Averaging pairs of numbers.mp4
9.5 MB
08. Probability theory/24. Unsupervised learning Averaging pairs of numbers.vtt
3.1 kB
08. Probability theory/3. Computing probabilities.mp4
38 MB
08. Probability theory/3. Computing probabilities.vtt
14 kB
08. Probability theory/4. Code compute probabilities.mp4
148 MB
08. Probability theory/4. Code compute probabilities.vtt
21 kB
08. Probability theory/5. Probability and odds.mp4
12 MB
08. Probability theory/5. Probability and odds.vtt
6.8 kB
08. Probability theory/6. Unsupervised learning probabilities of odds-space.mp4
5.9 MB
08. Probability theory/6. Unsupervised learning probabilities of odds-space.vtt
3.2 kB
08. Probability theory/7. Probability mass vs. density.mp4
134 MB
08. Probability theory/7. Probability mass vs. density.vtt
18 kB
08. Probability theory/8. Code compute probability mass functions.mp4
66 MB
08. Probability theory/8. Code compute probability mass functions.vtt
17 kB
08. Probability theory/9. Cumulative distribution functions.mp4
45 MB
08. Probability theory/9. Cumulative distribution functions.vtt
20 kB
09. Hypothesis testing/1. IVs, DVs, models, and other stats lingo.mp4
91 MB
09. Hypothesis testing/1. IVs, DVs, models, and other stats lingo.vtt
24 kB
09. Hypothesis testing/10. Statistical vs. theoretical vs. clinical significance.mp4
19 MB
09. Hypothesis testing/10. Statistical vs. theoretical vs. clinical significance.vtt
9.7 kB
09. Hypothesis testing/11. Cross-validation.mp4
28 MB
09. Hypothesis testing/11. Cross-validation.vtt
17 kB
09. Hypothesis testing/12. Statistical significance vs. classification accuracy.mp4
42 MB
09. Hypothesis testing/12. Statistical significance vs. classification accuracy.vtt
16 kB
09. Hypothesis testing/2. What is an hypothesis and how do you specify one.mp4
49 MB
09. Hypothesis testing/2. What is an hypothesis and how do you specify one.vtt
21 kB
09. Hypothesis testing/3. Sample distributions under null and alternative hypotheses.mp4
44 MB
09. Hypothesis testing/3. Sample distributions under null and alternative hypotheses.vtt
14 kB
09. Hypothesis testing/4. P-values definition, tails, and misinterpretations.mp4
106 MB
09. Hypothesis testing/4. P-values definition, tails, and misinterpretations.vtt
26 kB
09. Hypothesis testing/5. P-z combinations that you should memorize.mp4
17 MB
09. Hypothesis testing/5. P-z combinations that you should memorize.vtt
8.5 kB
09. Hypothesis testing/6. Degrees of freedom.mp4
33 MB
09. Hypothesis testing/7. Type 1 and Type 2 errors.mp4
46 MB
09. Hypothesis testing/7. Type 1 and Type 2 errors.vtt
21 kB
09. Hypothesis testing/8. Parametric vs. non-parametric tests.mp4
88 MB
09. Hypothesis testing/8. Parametric vs. non-parametric tests.vtt
13 kB
09. Hypothesis testing/9. Multiple comparisons and Bonferroni correction.mp4
75 MB
09. Hypothesis testing/9. Multiple comparisons and Bonferroni correction.vtt
18 kB
10. The t-test family/1. Purpose and interpretation of the t-test.mp4
32 MB
10. The t-test family/1. Purpose and interpretation of the t-test.vtt
19 kB
10. The t-test family/10. Mann-Whitney U test (nonparametric t-test).mp4
20 MB
10. The t-test family/10. Mann-Whitney U test (nonparametric t-test).vtt
8.5 kB
10. The t-test family/11. Code Mann-Whitney U test.mp4
52 MB
10. The t-test family/11. Code Mann-Whitney U test.vtt
7.4 kB
10. The t-test family/12. Permutation testing for t-test significance.mp4
64 MB
10. The t-test family/12. Permutation testing for t-test significance.vtt
16 kB
10. The t-test family/13. Code permutation testing.mp4
241 MB
10. The t-test family/13. Code permutation testing.vtt
35 kB
10. The t-test family/14. Unsupervised learning How many permutations.mp4
32 MB
10. The t-test family/14. Unsupervised learning How many permutations.vtt
7.6 kB
10. The t-test family/2. One-sample t-test.mp4
54 MB
10. The t-test family/2. One-sample t-test.vtt
11 kB
10. The t-test family/3. Code One-sample t-test.mp4
158 MB
10. The t-test family/3. Code One-sample t-test.vtt
29 kB
10. The t-test family/4. Unsupervised learning The role of variance.mp4
29 MB
10. The t-test family/4. Unsupervised learning The role of variance.vtt
4.1 kB
10. The t-test family/5. Two-samples t-test.mp4
94 MB
10. The t-test family/5. Two-samples t-test.vtt
19 kB
10. The t-test family/6. Code Two-samples t-test.mp4
211 MB
10. The t-test family/6. Code Two-samples t-test.vtt
30 kB
10. The t-test family/7. Unsupervised learning Importance of N for t-test.mp4
17 MB
10. The t-test family/7. Unsupervised learning Importance of N for t-test.vtt
6.6 kB
10. The t-test family/8. Wilcoxon signed-rank (nonparametric t-test).mp4
26 MB
10. The t-test family/8. Wilcoxon signed-rank (nonparametric t-test).vtt
10 kB
10. The t-test family/9. Code Signed-rank test.mp4
162 MB
10. The t-test family/9. Code Signed-rank test.vtt
25 kB
11. Confidence intervals on parameters/1. What are confidence intervals and why do we need them.mp4
30 MB
11. Confidence intervals on parameters/1. What are confidence intervals and why do we need them.vtt
12 kB
11. Confidence intervals on parameters/2. Computing confidence intervals via formula.mp4
17 MB
11. Confidence intervals on parameters/2. Computing confidence intervals via formula.vtt
9.3 kB
11. Confidence intervals on parameters/3. Code compute confidence intervals by formula.mp4
94 MB
11. Confidence intervals on parameters/3. Code compute confidence intervals by formula.vtt
23 kB
11. Confidence intervals on parameters/4. Confidence intervals via bootstrapping (resampling).mp4
54 MB
11. Confidence intervals on parameters/4. Confidence intervals via bootstrapping (resampling).vtt
13 kB
11. Confidence intervals on parameters/5. Code bootstrapping confidence intervals.mp4
137 MB
11. Confidence intervals on parameters/5. Code bootstrapping confidence intervals.vtt
20 kB
11. Confidence intervals on parameters/6. Unsupervised learning Confidence intervals for variance.mp4
8.5 MB
11. Confidence intervals on parameters/6. Unsupervised learning Confidence intervals for variance.vtt
2.0 kB
11. Confidence intervals on parameters/7. Misconceptions about confidence intervals.mp4
19 MB
11. Confidence intervals on parameters/7. Misconceptions about confidence intervals.vtt
8.8 kB
12. Correlation/1. Motivation and description of correlation.mp4
118 MB
12. Correlation/1. Motivation and description of correlation.vtt
26 kB
12. Correlation/10. Code partial correlation.mp4
108 MB
12. Correlation/10. Code partial correlation.vtt
28 kB
12. Correlation/11. The problem with Pearson.mp4
17 MB
12. Correlation/11. The problem with Pearson.vtt
10 kB
12. Correlation/12. Nonparametric correlation Spearman rank.mp4
24 MB
12. Correlation/12. Nonparametric correlation Spearman rank.vtt
10 kB
12. Correlation/13. Fisher-Z transformation for correlations.mp4
28 MB
12. Correlation/13. Fisher-Z transformation for correlations.vtt
10 kB
12. Correlation/14. Code Spearman correlation and Fisher-Z.mp4
43 MB
12. Correlation/14. Code Spearman correlation and Fisher-Z.vtt
11 kB
12. Correlation/15. Unsupervised learning Spearman correlation.mp4
16 MB
12. Correlation/15. Unsupervised learning Spearman correlation.vtt
2.1 kB
12. Correlation/16. Unsupervised learning confidence interval on correlation.mp4
10 MB
12. Correlation/16. Unsupervised learning confidence interval on correlation.vtt
3.4 kB
12. Correlation/17. Kendall's correlation for ordinal data.mp4
32 MB
12. Correlation/17. Kendall's correlation for ordinal data.vtt
15 kB
12. Correlation/18. Code Kendall correlation.mp4
184 MB
12. Correlation/18. Code Kendall correlation.vtt
25 kB
12. Correlation/19. Unsupervised learning Does Kendall vs. Pearson matter.mp4
15 MB
12. Correlation/19. Unsupervised learning Does Kendall vs. Pearson matter.vtt
3.6 kB
12. Correlation/2. Covariance and correlation formulas.mp4
40 MB
12. Correlation/2. Covariance and correlation formulas.vtt
18 kB
12. Correlation/20. The subgroups correlation paradox.mp4
22 MB
12. Correlation/20. The subgroups correlation paradox.vtt
7.0 kB
12. Correlation/21. Cosine similarity.mp4
14 MB
12. Correlation/21. Cosine similarity.vtt
8.1 kB
12. Correlation/22. Code Cosine similarity vs. Pearson correlation.mp4
102 MB
12. Correlation/22. Code Cosine similarity vs. Pearson correlation.vtt
30 kB
12. Correlation/3. Code correlation coefficient.mp4
214 MB
12. Correlation/3. Code correlation coefficient.vtt
38 kB
12. Correlation/4. Code Simulate data with specified correlation.mp4
70 MB
12. Correlation/4. Code Simulate data with specified correlation.vtt
19 kB
12. Correlation/5. Correlation matrix.mp4
31 MB
12. Correlation/5. Correlation matrix.vtt
13 kB
12. Correlation/6. Code correlation matrix.mp4
282 MB
12. Correlation/6. Code correlation matrix.vtt
29 kB
12. Correlation/7. Unsupervised learning average correlation matrices.mp4
18 MB
12. Correlation/7. Unsupervised learning average correlation matrices.vtt
4.1 kB
12. Correlation/8. Unsupervised learning correlation to covariance matrix.mp4
10 MB
12. Correlation/8. Unsupervised learning correlation to covariance matrix.vtt
5.8 kB
12. Correlation/9. Partial correlation.mp4
59 MB
12. Correlation/9. Partial correlation.vtt
15 kB
13. Analysis of Variance (ANOVA)/1. ANOVA intro, part1.mp4
138 MB
13. Analysis of Variance (ANOVA)/1. ANOVA intro, part1.vtt
25 kB
13. Analysis of Variance (ANOVA)/10. Two-way ANOVA example.mp4
36 MB
13. Analysis of Variance (ANOVA)/10. Two-way ANOVA example.vtt
16 kB
13. Analysis of Variance (ANOVA)/11. Code Two-way mixed ANOVA.mp4
114 MB
13. Analysis of Variance (ANOVA)/11. Code Two-way mixed ANOVA.vtt
20 kB
13. Analysis of Variance (ANOVA)/2. ANOVA intro, part 2.mp4
74 MB
13. Analysis of Variance (ANOVA)/2. ANOVA intro, part 2.vtt
28 kB
13. Analysis of Variance (ANOVA)/3. Sum of squares.mp4
46 MB
13. Analysis of Variance (ANOVA)/3. Sum of squares.vtt
26 kB
13. Analysis of Variance (ANOVA)/4. The F-test and the ANOVA table.mp4
20 MB
13. Analysis of Variance (ANOVA)/4. The F-test and the ANOVA table.vtt
11 kB
13. Analysis of Variance (ANOVA)/5. The omnibus F-test and post-hoc comparisons.mp4
63 MB
13. Analysis of Variance (ANOVA)/5. The omnibus F-test and post-hoc comparisons.vtt
18 kB
13. Analysis of Variance (ANOVA)/6. The two-way ANOVA.mp4
130 MB
13. Analysis of Variance (ANOVA)/6. The two-way ANOVA.vtt
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13. Analysis of Variance (ANOVA)/7. One-way ANOVA example.mp4
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13. Analysis of Variance (ANOVA)/7. One-way ANOVA example.vtt
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13. Analysis of Variance (ANOVA)/8. Code One-way ANOVA (independent samples).mp4
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13. Analysis of Variance (ANOVA)/8. Code One-way ANOVA (independent samples).vtt
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13. Analysis of Variance (ANOVA)/9. Code One-way repeated-measures ANOVA.mp4
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13. Analysis of Variance (ANOVA)/9. Code One-way repeated-measures ANOVA.vtt
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14. Regression/1. Introduction to GLM regression.mp4
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14. Regression/1. Introduction to GLM regression.vtt
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14. Regression/10. Polynomial regression models.mp4
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14. Regression/10. Polynomial regression models.vtt
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14. Regression/11. Code polynomial modeling.mp4
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14. Regression/11. Code polynomial modeling.vtt
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14. Regression/12. Unsupervised learning Polynomial design matrix.mp4
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14. Regression/12. Unsupervised learning Polynomial design matrix.vtt
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14. Regression/13. Logistic regression.mp4
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14. Regression/13. Logistic regression.vtt
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14. Regression/14. Code Logistic regression.mp4
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14. Regression/14. Code Logistic regression.vtt
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14. Regression/15. Under- and over-fitting.mp4
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14. Regression/15. Under- and over-fitting.vtt
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14. Regression/16. Unsupervised learning Overfit data.mp4
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14. Regression/16. Unsupervised learning Overfit data.vtt
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14. Regression/17. Comparing nested models.mp4
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14. Regression/18. What to do about missing data.mp4
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14. Regression/18. What to do about missing data.vtt
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14. Regression/2. Least-squares solution to the GLM.mp4
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14. Regression/2. Least-squares solution to the GLM.vtt
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14. Regression/3. Evaluating regression models R2 and F.mp4
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14. Regression/4. Simple regression.mp4
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14. Regression/6. Unsupervised learning Compute R2 and F.mp4
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14. Regression/7. Multiple regression.mp4
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14. Regression/8. Standardizing regression coefficients.mp4
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14. Regression/9. Code Multiple regression.mp4
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14. Regression/9. Code Multiple regression.vtt
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15. Statistical power and sample sizes/1. What is statistical power and why is it important.mp4
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15. Statistical power and sample sizes/1. What is statistical power and why is it important.vtt
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15. Statistical power and sample sizes/2. Estimating statistical power and sample size.mp4
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15. Statistical power and sample sizes/3. Compute power and sample size using GPower.mp4
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15. Statistical power and sample sizes/3. Compute power and sample size using GPower.vtt
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16. Clustering and dimension-reduction/1. K-means clustering.mp4
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16. Clustering and dimension-reduction/1. K-means clustering.vtt
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16. Clustering and dimension-reduction/10. Principal components analysis (PCA).mp4
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16. Clustering and dimension-reduction/10. Principal components analysis (PCA).vtt
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16. Clustering and dimension-reduction/11. Code PCA.mp4
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16. Clustering and dimension-reduction/12. Unsupervised learning K-means on PC data.mp4
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16. Clustering and dimension-reduction/13. Independent components analysis (ICA).mp4
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16. Clustering and dimension-reduction/2. Code k-means clustering.mp4
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16. Clustering and dimension-reduction/3. Unsupervised learning K-means and normalization.mp4
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16. Clustering and dimension-reduction/3. Unsupervised learning K-means and normalization.vtt
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16. Clustering and dimension-reduction/4. Unsupervised learning K-means on a Gauss blur.mp4
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16. Clustering and dimension-reduction/5. Clustering via dbscan.mp4
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16. Clustering and dimension-reduction/6. Code dbscan.mp4
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17. Signal detection theory/1. The two perspectives of the world.mp4
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18. A real-world data journey/1. Note about the code for this section.html
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18. A real-world data journey/10. Take-home messages.mp4
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18. A real-world data journey/10. Take-home messages.vtt
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18. A real-world data journey/2. Introduction.mp4
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18. A real-world data journey/2. Introduction.vtt
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18. A real-world data journey/3. MATLAB Import and clean the marriage data.mp4
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18. A real-world data journey/3. MATLAB Import and clean the marriage data.vtt
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18. A real-world data journey/3. state-marriage-rates-90-95-99-19.xlsx
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18. A real-world data journey/4. MATLAB Import the divorce data.mp4
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18. A real-world data journey/4. state-divorce-rates-90-95-99-19.xlsx
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18. A real-world data journey/5. MATLAB More data visualizations.mp4
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18. A real-world data journey/6. MATLAB Inferential statistics.mp4
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18. A real-world data journey/7. Python Import and clean the marriage data.mp4
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18. A real-world data journey/8. Python Import the divorce data.mp4
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18. A real-world data journey/9. Python Inferential statistics.mp4
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19. Bonus section/1. About deep learning.html
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19. Bonus section/2. Bonus content.html
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