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Master statistics & machine learning: intuition, math, code [3/2025, ENG]

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Master statistics & machine learning: intuition, math, code [3/2025, ENG] (Size: 12.98 GB)
  01. Introductions
  1. Important Getting the most out of this course.mp4 44.86 MB
  1. Important Getting the most out of this course.vtt 6.38 KB
  2. About using MATLAB or Python.mp4 11.54 MB
  2. About using MATLAB or Python.vtt 5.83 KB
  3. Statistics guessing game!.mp4 48.39 MB
  3. Statistics guessing game!.vtt 13.09 KB
  3. stats_intro_GuessTheTest.zip 3.72 KB
  4. Using the Q&A forum.mp4 24.36 MB
  4. Using the Q&A forum.vtt 7.95 KB
  5. (optional) Entering time-stamped notes in the Udemy video player.mp4 7.06 MB
  5. (optional) Entering time-stamped notes in the Udemy video player.vtt 3 KB
  02. Math prerequisites
  1. Should you memorize statistical formulas.mp4 28 MB
  1. Should you memorize statistical formulas.vtt 4.33 KB
  2. Arithmetic and exponents.mp4 7.55 MB
  2. Arithmetic and exponents.vtt 5.58 KB
  3. Scientific notation.mp4 12.87 MB
  3. Scientific notation.vtt 7.86 KB
  4. Summation notation.mp4 7.73 MB
  4. Summation notation.vtt 5.82 KB
  5. Absolute value.mp4 6.92 MB
  5. Absolute value.vtt 4.23 KB
  6. Natural exponent and logarithm.mp4 17.66 MB
  6. Natural exponent and logarithm.vtt 10.95 KB
  7. The logistic function.mp4 17.9 MB
  7. The logistic function.vtt 12.45 KB
  8. Rank and tied-rank.mp4 13.64 MB
  8. Rank and tied-rank.vtt 8.91 KB
  03. IMPORTANT Download course materials
  1. Download materials for the entire course!.mp4 51.17 MB
  1. Download materials for the entire course!.vtt 7.08 KB
  1. Link-to-code-on-github.txt 47 B
  1. Statistics_course-main.zip 2.5 MB
  04. What are (is) data
  1. Is data singular or plural!!!!.mp4 10.92 MB
  1. Is data singular or plural!!!!.vtt 2.42 KB
  2. Where do data come from and what do they mean.mp4 35.54 MB
  2. Where do data come from and what do they mean.vtt 8.23 KB
  3. Types of data categorical, numerical, etc.mp4 59.37 MB
  3. Types of data categorical, numerical, etc.vtt 20.27 KB
  4. Code representing types of data on computers.mp4 47.83 MB
  4. Code representing types of data on computers.vtt 12.12 KB
  5. Sample vs. population data.mp4 37.06 MB
  5. Sample vs. population data.vtt 16.99 KB
  6. Samples, case reports, and anecdotes.mp4 17.79 MB
  6. Samples, case reports, and anecdotes.vtt 7.85 KB
  7. The ethics of making up data.mp4 19.65 MB
  7. The ethics of making up data.vtt 10.14 KB
  05. Visualizing data
  1. Bar plots.mp4 36.83 MB
  1. Bar plots.vtt 15.28 KB
  10. Code pie charts.mp4 78.92 MB
  10. Code pie charts.vtt 18.79 KB
  11. When to use lines instead of bars.mp4 17.98 MB
  11. When to use lines instead of bars.vtt 8.25 KB
  12. Linear vs. logarithmic axis scaling.mp4 25.64 MB
  12. Linear vs. logarithmic axis scaling.vtt 11.95 KB
  13. Code line plots.mp4 37.29 MB
  13. Code line plots.vtt 10.62 KB
  14. Unsupervised learning log-scaled plots.mp4 3.73 MB
  14. Unsupervised learning log-scaled plots.vtt 2.41 KB
  2. Code bar plots.mp4 100.03 MB
  2. Code bar plots.vtt 22.66 KB
  3. Box-and-whisker plots.mp4 11.12 MB
  3. Box-and-whisker plots.vtt 7.74 KB
  4. Code box plots.mp4 83.65 MB
  4. Code box plots.vtt 11.83 KB
  5. Unsupervised learning Boxplots of normal and uniform noise.mp4 8.24 MB
  5. Unsupervised learning Boxplots of normal and uniform noise.vtt 3.72 KB
  6. Histograms.mp4 43.73 MB
  6. Histograms.vtt 15.49 KB
  7. Code histograms.mp4 133.49 MB
  7. Code histograms.vtt 23.12 KB
  8. Unsupervised learning Histogram proportion.mp4 11.79 MB
  8. Unsupervised learning Histogram proportion.vtt 3.39 KB
  9. Pie charts.mp4 16.53 MB
  9. Pie charts.vtt 7.99 KB
  06. Descriptive statistics
  1. Descriptive vs. inferential statistics.mp4 21.48 MB
  1. Descriptive vs. inferential statistics.vtt 6.23 KB
  10. Unsupervised learning central tendencies with outliers.mp4 16.74 MB
  10. Unsupervised learning central tendencies with outliers.vtt 4.34 KB
  11. Measures of dispersion (variance, standard deviation).mp4 56.95 MB
  11. Measures of dispersion (variance, standard deviation).vtt 25.77 KB
  12. Code Computing dispersion.mp4 266.1 MB
  12. Code Computing dispersion.vtt 36.1 KB
  13. Interquartile range (IQR).mp4 9.84 MB
  13. Interquartile range (IQR).vtt 6.71 KB
  14. Code IQR.mp4 83.39 MB
  14. Code IQR.vtt 21.5 KB
  15. QQ plots.mp4 16.21 MB
  15. QQ plots.vtt 10.08 KB
  16. Code QQ plots.mp4 90.3 MB
  16. Code QQ plots.vtt 22.28 KB
  17. Statistical moments.mp4 21.68 MB
  17. Statistical moments.vtt 11.87 KB
  18. Histograms part 2 Number of bins.mp4 23.5 MB
  18. Histograms part 2 Number of bins.vtt 13.95 KB
  19. Code Histogram bins.mp4 118.12 MB
  19. Code Histogram bins.vtt 17.56 KB
  2. Accuracy, precision, resolution.mp4 25.42 MB
  2. Accuracy, precision, resolution.vtt 10.93 KB
  20. Violin plots.mp4 6.47 MB
  20. Violin plots.vtt 4.71 KB
  21. Code violin plots.mp4 104.96 MB
  21. Code violin plots.vtt 14.49 KB
  22. Unsupervised learning asymmetric violin plots.mp4 17.32 MB
  22. Unsupervised learning asymmetric violin plots.vtt 3.65 KB
  23. Shannon entropy.mp4 33.05 MB
  23. Shannon entropy.vtt 15.24 KB
  24. Code entropy.mp4 96.76 MB
  24. Code entropy.vtt 27.66 KB
  25. Unsupervised learning entropy and number of bins.mp4 8.25 MB
  25. Unsupervised learning entropy and number of bins.vtt 2.03 KB
  3. Data distributions.mp4 31.95 MB
  3. Data distributions.vtt 16.36 KB
  4. Code data from different distributions.mp4 303.12 MB
  4. Code data from different distributions.vtt 43.69 KB
  5. Unsupervised learning histograms of distributions.mp4 10.18 MB
  5. Unsupervised learning histograms of distributions.vtt 2.8 KB
  6. The beauty and simplicity of Normal.mp4 10.23 MB
  6. The beauty and simplicity of Normal.vtt 7.97 KB
  7. Measures of central tendency (mean).mp4 38.71 MB
  7. Measures of central tendency (mean).vtt 17.87 KB
  8. Measures of central tendency (median, mode).mp4 34.26 MB
  8. Measures of central tendency (median, mode).vtt 17.56 KB
  9. Code computing central tendency.mp4 66.6 MB
  9. Code computing central tendency.vtt 19.24 KB
  07. Data normalizations and outliers
  1. Garbage in, garbage out (GIGO).mp4 11.55 MB
  1. Garbage in, garbage out (GIGO).vtt 5.76 KB
  10. Code z-score for outlier removal.mp4 136.9 MB
  10. Code z-score for outlier removal.vtt 31.78 KB
  11. Unsupervised learning z vs. modified-z.mp4 9.02 MB
  11. Unsupervised learning z vs. modified-z.vtt 3.92 KB
  12. Multivariate outlier detection.mp4 25.05 MB
  12. Multivariate outlier detection.vtt 13.4 KB
  13. Code Euclidean distance for outlier removal.mp4 43.72 MB
  13. Code Euclidean distance for outlier removal.vtt 12.36 KB
  14. Removing outliers by data trimming.mp4 16.9 MB
  14. Removing outliers by data trimming.vtt 8.29 KB
  15. Code Data trimming to remove outliers.mp4 65.29 MB
  15. Code Data trimming to remove outliers.vtt 15.4 KB
  16. Non-parametric solutions to outliers.mp4 22.96 MB
  16. Non-parametric solutions to outliers.vtt 6.43 KB
  17. Nonlinear data transformations.mp4 33.69 MB
  17. Nonlinear data transformations.vtt 20.46 KB
  18. An outlier lecture on personal accountability.mp4 17.7 MB
  18. An outlier lecture on personal accountability.vtt 4.34 KB
  2. Z-score standardization.mp4 36.23 MB
  2. Z-score standardization.vtt 13.69 KB
  3. Code z-score.mp4 66.77 MB
  3. Code z-score.vtt 18.15 KB
  4. Min-max scaling.mp4 11.73 MB
  4. Min-max scaling.vtt 7.06 KB
  5. Code min-max scaling.mp4 40.43 MB
  5. Code min-max scaling.vtt 11.29 KB
  6. Unsupervised learning Invert the min-max scaling.mp4 6.79 MB
  6. Unsupervised learning Invert the min-max scaling.vtt 3.66 KB
  7. What are outliers and why are they dangerous.mp4 43 MB
  7. What are outliers and why are they dangerous.vtt 20.79 KB
  8. Removing outliers z-score method.mp4 33.51 MB
  8. Removing outliers z-score method.vtt 13.65 KB
  9. The modified z-score method.mp4 9.62 MB
  9. The modified z-score method.vtt 5.85 KB
  08. Probability theory
  1. What is probability.mp4 41.11 MB
  1. What is probability.vtt 17.07 KB
  10. Code cdfs and pdfs.mp4 95.94 MB
  10. Code cdfs and pdfs.vtt 14.29 KB
  11. Unsupervised learning cdf's for various distributions.mp4 9.31 MB
  11. Unsupervised learning cdf's for various distributions.vtt 3.42 KB
  12. Creating sample estimate distributions.mp4 124.85 MB
  12. Creating sample estimate distributions.vtt 26.82 KB
  13. Monte Carlo sampling.mp4 8.83 MB
  13. Monte Carlo sampling.vtt 3.92 KB
  14. Sampling variability, noise, and other annoyances.mp4 106.08 MB
  14. Sampling variability, noise, and other annoyances.vtt 13.19 KB
  15. Code sampling variability.mp4 154.75 MB
  15. Code sampling variability.vtt 35.75 KB
  16. Expected value.mp4 59.63 MB
  16. Expected value.vtt 14.61 KB
  17. Conditional probability.mp4 85.68 MB
  17. Conditional probability.vtt 17.74 KB
  18. Code conditional probabilities.mp4 115.09 MB
  18. Code conditional probabilities.vtt 27.7 KB
  19. Tree diagrams for conditional probabilities.mp4 13.5 MB
  19. Tree diagrams for conditional probabilities.vtt 9.45 KB
  2. Probability vs. proportion.mp4 37.52 MB
  2. Probability vs. proportion.vtt 13.41 KB
  20. The Law of Large Numbers.mp4 40.55 MB
  20. The Law of Large Numbers.vtt 14.02 KB
  21. Code Law of Large Numbers in action.mp4 165.61 MB
  21. Code Law of Large Numbers in action.vtt 25.99 KB
  22. The Central Limit Theorem.mp4 26.67 MB
  22. The Central Limit Theorem.vtt 15.52 KB
  23. Code the CLT in action.mp4 93.33 MB
  23. Code the CLT in action.vtt 22.62 KB
  24. Unsupervised learning Averaging pairs of numbers.mp4 9.48 MB
  24. Unsupervised learning Averaging pairs of numbers.vtt 3.08 KB
  3. Computing probabilities.mp4 37.53 MB
  3. Computing probabilities.vtt 14.11 KB
  4. Code compute probabilities.mp4 148.4 MB
  4. Code compute probabilities.vtt 20.88 KB
  5. Probability and odds.mp4 12.01 MB
  5. Probability and odds.vtt 6.78 KB
  6. Unsupervised learning probabilities of odds-space.mp4 5.92 MB
  6. Unsupervised learning probabilities of odds-space.vtt 3.16 KB
  7. Probability mass vs. density.mp4 134.14 MB
  7. Probability mass vs. density.vtt 17.8 KB
  8. Code compute probability mass functions.mp4 66.17 MB
  8. Code compute probability mass functions.vtt 16.56 KB
  9. Cumulative distribution functions.mp4 45.41 MB
  9. Cumulative distribution functions.vtt 20.34 KB
  09. Hypothesis testing
  1. IVs, DVs, models, and other stats lingo.mp4 91.14 MB
  1. IVs, DVs, models, and other stats lingo.vtt 23.56 KB
  10. Statistical vs. theoretical vs. clinical significance.mp4 19.08 MB
  10. Statistical vs. theoretical vs. clinical significance.vtt 9.75 KB
  11. Cross-validation.mp4 27.96 MB
  11. Cross-validation.vtt 16.68 KB
  12. Statistical significance vs. classification accuracy.mp4 42.5 MB
  12. Statistical significance vs. classification accuracy.vtt 16.15 KB
  2. What is an hypothesis and how do you specify one.mp4 49.12 MB
  2. What is an hypothesis and how do you specify one.vtt 21.09 KB
  3. Sample distributions under null and alternative hypotheses.mp4 43.75 MB
  3. Sample distributions under null and alternative hypotheses.vtt 14.48 KB
  4. P-values definition, tails, and misinterpretations.mp4 106.47 MB
  4. P-values definition, tails, and misinterpretations.vtt 25.67 KB
  5. P-z combinations that you should memorize.mp4 17.32 MB
  5. P-z combinations that you should memorize.vtt 8.48 KB
  6. Degrees of freedom.mp4 32.9 MB
  7. Type 1 and Type 2 errors.mp4 45.91 MB
  7. Type 1 and Type 2 errors.vtt 20.8 KB
  8. Parametric vs. non-parametric tests.mp4 87.45 MB
  8. Parametric vs. non-parametric tests.vtt 13.17 KB
  9. Multiple comparisons and Bonferroni correction.mp4 75.41 MB
  9. Multiple comparisons and Bonferroni correction.vtt 18.25 KB
  10. The t-test family
  1. Purpose and interpretation of the t-test.mp4 32.16 MB
  1. Purpose and interpretation of the t-test.vtt 18.73 KB
  10. Mann-Whitney U test (nonparametric t-test).mp4 20.32 MB
  10. Mann-Whitney U test (nonparametric t-test).vtt 8.47 KB
  11. Code Mann-Whitney U test.mp4 52.05 MB
  11. Code Mann-Whitney U test.vtt 7.4 KB
  12. Permutation testing for t-test significance.mp4 63.48 MB
  12. Permutation testing for t-test significance.vtt 16.42 KB
  13. Code permutation testing.mp4 240.9 MB
  13. Code permutation testing.vtt 35 KB
  14. Unsupervised learning How many permutations.mp4 32.5 MB
  14. Unsupervised learning How many permutations.vtt 7.64 KB
  2. One-sample t-test.mp4 53.95 MB
  2. One-sample t-test.vtt 11.13 KB
  3. Code One-sample t-test.mp4 157.96 MB
  3. Code One-sample t-test.vtt 28.72 KB
  4. Unsupervised learning The role of variance.mp4 28.65 MB
  4. Unsupervised learning The role of variance.vtt 4.12 KB
  5. Two-samples t-test.mp4 93.81 MB
  5. Two-samples t-test.vtt 18.63 KB
  6. Code Two-samples t-test.mp4 211.35 MB
  6. Code Two-samples t-test.vtt 30.23 KB
  7. Unsupervised learning Importance of N for t-test.mp4 16.77 MB
  7. Unsupervised learning Importance of N for t-test.vtt 6.65 KB
  8. Wilcoxon signed-rank (nonparametric t-test).mp4 25.98 MB
  8. Wilcoxon signed-rank (nonparametric t-test).vtt 10.28 KB
  9. Code Signed-rank test.mp4 161.85 MB
  9. Code Signed-rank test.vtt 25.01 KB
  11. Confidence intervals on parameters
  1. What are confidence intervals and why do we need them.mp4 29.83 MB
  1. What are confidence intervals and why do we need them.vtt 12.33 KB
  2. Computing confidence intervals via formula.mp4 17.33 MB
  2. Computing confidence intervals via formula.vtt 9.34 KB
  3. Code compute confidence intervals by formula.mp4 94.3 MB
  3. Code compute confidence intervals by formula.vtt 23.34 KB
  4. Confidence intervals via bootstrapping (resampling).mp4 54.27 MB
  4. Confidence intervals via bootstrapping (resampling).vtt 12.88 KB
  5. Code bootstrapping confidence intervals.mp4 136.71 MB
  5. Code bootstrapping confidence intervals.vtt 19.83 KB
  6. Unsupervised learning Confidence intervals for variance.mp4 8.54 MB
  6. Unsupervised learning Confidence intervals for variance.vtt 2.02 KB
  7. Misconceptions about confidence intervals.mp4 18.61 MB
  7. Misconceptions about confidence intervals.vtt 8.81 KB
  12. Correlation
  1. Motivation and description of correlation.mp4 118.43 MB
  1. Motivation and description of correlation.vtt 26.32 KB
  10. Code partial correlation.mp4 108.27 MB
  10. Code partial correlation.vtt 27.54 KB
  11. The problem with Pearson.mp4 16.57 MB
  11. The problem with Pearson.vtt 10.01 KB
  12. Nonparametric correlation Spearman rank.mp4 23.72 MB
  12. Nonparametric correlation Spearman rank.vtt 10.54 KB
  13. Fisher-Z transformation for correlations.mp4 28.48 MB
  13. Fisher-Z transformation for correlations.vtt 9.99 KB
  14. Code Spearman correlation and Fisher-Z.mp4 42.71 MB
  14. Code Spearman correlation and Fisher-Z.vtt 10.85 KB
  15. Unsupervised learning Spearman correlation.mp4 15.95 MB
  15. Unsupervised learning Spearman correlation.vtt 2.1 KB
  16. Unsupervised learning confidence interval on correlation.mp4 10.31 MB
  16. Unsupervised learning confidence interval on correlation.vtt 3.42 KB
  17. Kendall's correlation for ordinal data.mp4 32.43 MB
  17. Kendall's correlation for ordinal data.vtt 14.84 KB
  18. Code Kendall correlation.mp4 184.22 MB
  18. Code Kendall correlation.vtt 24.66 KB
  19. Unsupervised learning Does Kendall vs. Pearson matter.mp4 14.95 MB
  19. Unsupervised learning Does Kendall vs. Pearson matter.vtt 3.6 KB
  2. Covariance and correlation formulas.mp4 39.99 MB
  2. Covariance and correlation formulas.vtt 17.69 KB
  20. The subgroups correlation paradox.mp4 21.57 MB
  20. The subgroups correlation paradox.vtt 7.04 KB
  21. Cosine similarity.mp4 14.2 MB
  21. Cosine similarity.vtt 8.15 KB
  22. Code Cosine similarity vs. Pearson correlation.mp4 102.19 MB
  22. Code Cosine similarity vs. Pearson correlation.vtt 29.5 KB
  3. Code correlation coefficient.mp4 214.14 MB
  3. Code correlation coefficient.vtt 38.04 KB
  4. Code Simulate data with specified correlation.mp4 70.12 MB
  4. Code Simulate data with specified correlation.vtt 19.2 KB
  5. Correlation matrix.mp4 30.96 MB
  5. Correlation matrix.vtt 13.13 KB
  6. Code correlation matrix.mp4 282.48 MB
  6. Code correlation matrix.vtt 29.32 KB
  7. Unsupervised learning average correlation matrices.mp4 18.49 MB
  7. Unsupervised learning average correlation matrices.vtt 4.1 KB
  8. Unsupervised learning correlation to covariance matrix.mp4 10.14 MB
  8. Unsupervised learning correlation to covariance matrix.vtt 5.85 KB
  9. Partial correlation.mp4 59.34 MB
  9. Partial correlation.vtt 15.21 KB
  13. Analysis of Variance (ANOVA)
  1. ANOVA intro, part1.mp4 137.72 MB
  1. ANOVA intro, part1.vtt 25.41 KB
  10. Two-way ANOVA example.mp4 35.95 MB
  10. Two-way ANOVA example.vtt 16.12 KB
  11. Code Two-way mixed ANOVA.mp4 114.16 MB
  11. Code Two-way mixed ANOVA.vtt 19.77 KB
  2. ANOVA intro, part 2.mp4 73.92 MB
  2. ANOVA intro, part 2.vtt 27.63 KB
  3. Sum of squares.mp4 45.88 MB
  3. Sum of squares.vtt 25.64 KB
  4. The F-test and the ANOVA table.mp4 19.92 MB
  4. The F-test and the ANOVA table.vtt 10.71 KB
  5. The omnibus F-test and post-hoc comparisons.mp4 63.36 MB
  5. The omnibus F-test and post-hoc comparisons.vtt 18.04 KB
  6. The two-way ANOVA.mp4 130.53 MB
  6. The two-way ANOVA.vtt 29.43 KB
  7. One-way ANOVA example.mp4 44.32 MB
  7. One-way ANOVA example.vtt 19.88 KB
  8. Code One-way ANOVA (independent samples).mp4 172.72 MB
  8. Code One-way ANOVA (independent samples).vtt 23.51 KB
  9. Code One-way repeated-measures ANOVA.mp4 73.1 MB
  9. Code One-way repeated-measures ANOVA.vtt 17.71 KB
  14. Regression
  1. Introduction to GLM regression.mp4 61.98 MB
  1. Introduction to GLM regression.vtt 28.9 KB
  10. Polynomial regression models.mp4 48.15 MB
  10. Polynomial regression models.vtt 12.18 KB
  11. Code polynomial modeling.mp4 129.08 MB
  11. Code polynomial modeling.vtt 21.34 KB
  12. Unsupervised learning Polynomial design matrix.mp4 4.74 MB
  12. Unsupervised learning Polynomial design matrix.vtt 1.14 KB
  13. Logistic regression.mp4 52.71 MB
  13. Logistic regression.vtt 23.5 KB
  14. Code Logistic regression.mp4 81.23 MB
  14. Code Logistic regression.vtt 13.2 KB
  15. Under- and over-fitting.mp4 120.86 MB
  15. Under- and over-fitting.vtt 23.87 KB
  16. Unsupervised learning Overfit data.mp4 4.82 MB
  16. Unsupervised learning Overfit data.vtt 2.72 KB
  17. Comparing nested models.mp4 41.33 MB
  17. Comparing nested models.vtt 17.38 KB
  18. What to do about missing data.mp4 16.05 MB
  18. What to do about missing data.vtt 9.42 KB
  2. Least-squares solution to the GLM.mp4 41.41 MB
  2. Least-squares solution to the GLM.vtt 13.62 KB
  3. Evaluating regression models R2 and F.mp4 62.77 MB
  3. Evaluating regression models R2 and F.vtt 19.73 KB
  4. Simple regression.mp4 36.77 MB
  4. Simple regression.vtt 18.46 KB
  5. Code simple regression.mp4 52.29 MB
  5. Code simple regression.vtt 12.74 KB
  6. Unsupervised learning Compute R2 and F.mp4 5.38 MB
  6. Unsupervised learning Compute R2 and F.vtt 1.48 KB
  7. Multiple regression.mp4 45.14 MB
  7. Multiple regression.vtt 18.34 KB
  8. Standardizing regression coefficients.mp4 75.19 MB
  8. Standardizing regression coefficients.vtt 17.26 KB
  9. Code Multiple regression.mp4 170.95 MB
  9. Code Multiple regression.vtt 26.59 KB
  15. Statistical power and sample sizes
  1. What is statistical power and why is it important.mp4 40.22 MB
  1. What is statistical power and why is it important.vtt 14.93 KB
  2. Estimating statistical power and sample size.mp4 36.08 MB
  2. Estimating statistical power and sample size.vtt 15.86 KB
  3. Compute power and sample size using GPower.mp4 31.2 MB
  3. Compute power and sample size using GPower.vtt 6.08 KB
  16. Clustering and dimension-reduction
  1. K-means clustering.mp4 54.29 MB
  1. K-means clustering.vtt 20.28 KB
  10. Principal components analysis (PCA).mp4 42.56 MB
  10. Principal components analysis (PCA).vtt 23.86 KB
  11. Code PCA.mp4 175.1 MB
  11. Code PCA.vtt 24.46 KB
  12. Unsupervised learning K-means on PC data.mp4 11.52 MB
  12. Unsupervised learning K-means on PC data.vtt 2.27 KB
  13. Independent components analysis (ICA).mp4 45.52 MB
  13. Independent components analysis (ICA).vtt 17.41 KB
  14. Code ICA.mp4 73.36 MB
  14. Code ICA.vtt 17.28 KB
  2. Code k-means clustering.mp4 230.34 MB
  2. Code k-means clustering.vtt 32.55 KB
  3. Unsupervised learning K-means and normalization.mp4 12.91 MB
  3. Unsupervised learning K-means and normalization.vtt 2.63 KB
  4. Unsupervised learning K-means on a Gauss blur.mp4 7.94 MB
  4. Unsupervised learning K-means on a Gauss blur.vtt 2 KB
  5. Clustering via dbscan.mp4 100.3 MB
  5. Clustering via dbscan.vtt 20.84 KB
  6. Code dbscan.mp4 288.13 MB
  6. Code dbscan.vtt 46.07 KB
  7. Unsupervised learning dbscan vs. k-means.mp4 19.94 MB
  7. Unsupervised learning dbscan vs. k-means.vtt 4.37 KB
  8. K-nearest neighbor classification.mp4 12.48 MB
  8. K-nearest neighbor classification.vtt 9.02 KB
  9. Code KNN.mp4 108.34 MB
  9. Code KNN.vtt 16.32 KB
  17. Signal detection theory
  1. The two perspectives of the world.mp4 13.91 MB
  1. The two perspectives of the world.vtt 8.38 KB
  2. d-prime.mp4 59.64 MB
  2. d-prime.vtt 17.86 KB
  3. Code d-prime.mp4 69.51 MB
  3. Code d-prime.vtt 20.42 KB
  4. Response bias.mp4 21.82 MB
  4. Response bias.vtt 11.85 KB
  5. Code Response bias.mp4 22.81 MB
  5. Code Response bias.vtt 6.12 KB
  6. F-score.mp4 107.26 MB
  6. F-score.vtt 33.14 KB
  7. Receiver operating characteristics (ROC).mp4 64.37 MB
  7. Receiver operating characteristics (ROC).vtt 10.87 KB
  8. Code ROC curves.mp4 54.62 MB
  8. Code ROC curves.vtt 11.02 KB
  9. Unsupervised learning Make this plot look nicer!.mp4 11.5 MB
  9. Unsupervised learning Make this plot look nicer!.vtt 2.39 KB
  18. A real-world data journey
  1. Note about the code for this section.html 174 B
  10. Take-home messages.mp4 43.8 MB
  10. Take-home messages.vtt 8.48 KB
  2. Introduction.mp4 53.02 MB
  2. Introduction.vtt 6.02 KB
  3. MATLAB Import and clean the marriage data.mp4 201.3 MB
  3. MATLAB Import and clean the marriage data.vtt 23.71 KB
  3. state-marriage-rates-90-95-99-19.xlsx 23.64 KB
  4. MATLAB Import the divorce data.mp4 96.29 MB
  4. MATLAB Import the divorce data.vtt 11.91 KB
  4. state-divorce-rates-90-95-99-19.xlsx 22.47 KB
  5. MATLAB More data visualizations.mp4 34.32 MB
  5. MATLAB More data visualizations.vtt 9.42 KB
  6. MATLAB Inferential statistics.mp4 113.52 MB
  6. MATLAB Inferential statistics.vtt 14.91 KB
  7. Python Import and clean the marriage data.mp4 249.82 MB
  7. Python Import and clean the marriage data.vtt 29.52 KB
  8. Python Import the divorce data.mp4 137.14 MB
  8. Python Import the divorce data.vtt 18.01 KB
  9. Python Inferential statistics.mp4 115.54 MB
  9. Python Inferential statistics.vtt 16.29 KB
  19. Bonus section
  1. About deep learning.html 658 B
  2. Bonus content.html 4.72 KB
  ▲ 449 total files

Description


Обучающие видео » Компьютерные видеоуроки и обучающие интерактивные DVD » Machine/Deep Learning, Neural Networks

Master statistics & machine learning: intuition, math, code

Год выпуска: 3/2025
Производитель: Udemy
Сайт производителя: https://www.udemy.com/course/statsml_x/
Автор: Mike X Cohen
Продолжительность: 38h 20m 57s
Тип раздаваемого материала: Видеоурок
Язык: Английский
Субтитры: Английский
Описание:

What you'll learn Descriptive statistics (mean, variance, etc)
Inferential statistics
T-tests, correlation, ANOVA, regression, clustering
The math behind the "black box" statistical methods
How to implement statistical methods in code
How to interpret statistics correctly and avoid common misunderstandings
Coding techniques in Python and MATLAB/Octave
Machine learning methods like clustering, predictive analysis, classification, and data cleaning Requirements Good work ethic and motivation to learn.
Previous background in statistics or machine learning is not necessary.
Python -OR- MATLAB with the Statistics toolbox (or Octave).
Some coding familiarity for the optional code exercises.
No textbooks necessary! All materials are provided inside the course. Description

Statistics and probability control your life. I don't just mean What YouTube's algorithm recommends you to watch next, and I don't just mean the chance of meeting your future significant other in class or at a bar. Human behavior, single-cell organisms, Earthquakes, the stock market, whether it will snow in the first week of December, and countless other phenomena are probabilistic and statistical. Even the very nature of the most fundamental deep structure of the universe is governed by probability and statistics.

You need to understand statistics.

Nearly all areas of human civilization are incorporating code and numerical computations. This means that many jobs and areas of study are based on applications of statistical and machine-learning techniques in programming languages like Python and MATLAB. This is often called 'data science' and is an increasingly important topic. Statistics and machine learning are also fundamental to artificial intelligence (AI) and business intelligence.

If you want to make yourself a future-proof employee, employer, data scientist, or researcher in any technical field -- ranging from data scientist to engineering to research scientist to deep learning modeler -- you'll need to know statistics and machine-learning. And you'll need to know how to implement concepts like probability theory and confidence intervals, k-means clustering and PCA, Spearman correlation and logistic regression, in computer languages like Python or MATLAB.

There are six reasons why you should take this course: This course covers everything you need to understand the fundamentals of statistics, machine learning, and data science, from bar plots to ANOVAs, regression to k-means, t-test to non-parametric permutation testing.
After completing this course, you will be able to understand a wide range of statistical and machine-learning analyses, even specific advanced methods that aren't taught here. That's because you will learn the foundations upon which advanced methods are build.
This course balances mathematical rigor with intuitive explanations, and hands-on explorations in code.
Enrolling in the course gives you access to the Q&A, in which I actively participate every day.
I've been studying, developing, and teaching statistics for over 20 years, and I think math is, like, really cool. What you need to know before taking this course: High-school level maths. This is an applications-oriented course, so I don't go into a lot of detail about proofs, derivations, or calculus.
Basic coding skills in Python or MATLAB. This is necessary only if you want to follow along with the code. You can successfully complete this course without writing a single line of code! But participating in the coding exercises will help you learn the material. The MATLAB code relies on the Statistics and Machine Learning toolbox (you can use Octave if you don't have MATLAB or the statistics toolbox). Python code is written in Jupyter notebooks.
I recommend taking my free course called "Statistics literacy for non-statisticians". It's 90 minutes long and will give you a bird's-eye-view of the main topics in statistics that I go into much much much more detail about here in this course. Note that the free short course is not required for this course, but complements this course nicely. And you can get through the whole thing in less than an hour if you watch if on 1.5x speed!
You do not need any previous experience with statistics, machine learning, deep learning, or data science. That's why you're here! Is this course up to date?

Yes, I maintain all of my courses regularly. I add new lectures to keep the course "alive," and I add new lectures (or sometimes re-film existing lectures) to explain maths concepts better if students find a topic confusing or if I made a mistake in the lecture (rare, but it happens!).

You can check the "Last updated" text at the top of this page to see when I last worked on improving this course!

What if you have questions about the material?

This course has a Q&A (question and answer) section where you can post your questions about the course material (about the maths, statistics, coding, or machine learning aspects). I try to answer all questions within a day. You can also see all other questions and answers, which really improves how much you can learn! And you can contribute to the Q&A by posting to ongoing discussions.

And, you can also post your code for feedback or just to show off -- I love it when students actually write better code than me! (Ahem, doesn't happen so often.)

What should you do now?

First of all, congrats on reading this far; that means you are seriously interested in learning statistics and machine learning. Watch the preview videos, check out the reviews, and, when you're ready, invest in your brain by learning from this course!

Who this course is for: Students taking statistics or machine learning courses
Professionals who need to learn statistics and machine learning
Scientists who want to understand their data analyses
Anyone who wants to see "under the hood" of machine learning
Artificial intelligence (AI) students
Business intelligence students Формат видео: MP4
Видео: avc, 1920x1080, 16:9, 30.000 к/с, 423 кб/с
Аудио: aac lc sbr, 44.1 кгц, 62.8 кб/с, 2 аудио
[spoiler="Изменения/Changes"]
Version 2020/9 has increased by 3 lessons compared to 2020/6.

Version 2021/11 compared to 2020/9 has increased by approximately 14 lessons (1 section) and duration of 2 hours and 22 minutes. Its quality has also been upgraded from 720p to 1080p.

Version 2022/10 compared to 2021/11 has increased by 8 minutes.

The 2025/3 version has not changed in the number of lessons and duration compared to 2022/10, but it has been updated after 3 years.
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[spoiler="Скриншоты"]

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