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

磁力链接/BT种子名称

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

磁力链接/BT种子简介

种子哈希：65dc0cbe2b0e2976d6a9564f3a8c85b85c1d619b
文件大小： 12.98G
已经下载：143次
下载速度：极快
收录时间：2025-07-30
最近下载：2025-09-06

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文件列表

  • 06. Descriptive statistics/4. Code data from different distributions.mp4 317.8 MB
  • 16. Clustering and dimension-reduction/6. Code dbscan.mp4 302.1 MB
  • 12. Correlation/6. Code correlation matrix.mp4 296.2 MB
  • 06. Descriptive statistics/12. Code Computing dispersion.mp4 279.0 MB
  • 18. A real-world data journey/7. Python Import and clean the marriage data.mp4 262.0 MB
  • 10. The t-test family/13. Code permutation testing.mp4 252.6 MB
  • 16. Clustering and dimension-reduction/2. Code k-means clustering.mp4 241.5 MB
  • 12. Correlation/3. Code correlation coefficient.mp4 224.5 MB
  • 10. The t-test family/6. Code Two-samples t-test.mp4 221.6 MB
  • 18. A real-world data journey/3. MATLAB Import and clean the marriage data.mp4 211.1 MB
  • 12. Correlation/18. Code Kendall correlation.mp4 193.2 MB
  • 16. Clustering and dimension-reduction/11. Code PCA.mp4 183.6 MB
  • 13. Analysis of Variance (ANOVA)/8. Code One-way ANOVA (independent samples).mp4 181.1 MB
  • 14. Regression/9. Code Multiple regression.mp4 179.3 MB
  • 08. Probability theory/21. Code Law of Large Numbers in action.mp4 173.7 MB
  • 10. The t-test family/9. Code Signed-rank test.mp4 169.7 MB
  • 10. The t-test family/3. Code One-sample t-test.mp4 165.6 MB
  • 08. Probability theory/15. Code sampling variability.mp4 162.3 MB
  • 08. Probability theory/4. Code compute probabilities.mp4 155.6 MB
  • 13. Analysis of Variance (ANOVA)/1. ANOVA intro, part1.mp4 144.4 MB
  • 18. A real-world data journey/8. Python Import the divorce data.mp4 143.8 MB
  • 07. Data normalizations and outliers/10. Code z-score for outlier removal.mp4 143.5 MB
  • 11. Confidence intervals on parameters/5. Code bootstrapping confidence intervals.mp4 143.4 MB
  • 08. Probability theory/7. Probability mass vs. density.mp4 140.7 MB
  • 05. Visualizing data/7. Code histograms.mp4 140.0 MB
  • 13. Analysis of Variance (ANOVA)/6. The two-way ANOVA.mp4 136.9 MB
  • 14. Regression/11. Code polynomial modeling.mp4 135.4 MB
  • 08. Probability theory/12. Creating sample estimate distributions.mp4 130.9 MB
  • 14. Regression/15. Under- and over-fitting.mp4 126.7 MB
  • 12. Correlation/1. Motivation and description of correlation.mp4 124.2 MB
  • 06. Descriptive statistics/19. Code Histogram bins.mp4 123.9 MB
  • 18. A real-world data journey/9. Python Inferential statistics.mp4 121.2 MB
  • 08. Probability theory/18. Code conditional probabilities.mp4 120.7 MB
  • 13. Analysis of Variance (ANOVA)/11. Code Two-way mixed ANOVA.mp4 119.7 MB
  • 18. A real-world data journey/6. MATLAB Inferential statistics.mp4 119.0 MB
  • 16. Clustering and dimension-reduction/9. Code KNN.mp4 113.6 MB
  • 12. Correlation/10. Code partial correlation.mp4 113.5 MB
  • 17. Signal detection theory/6. F-score.mp4 112.5 MB
  • 09. Hypothesis testing/4. P-values definition, tails, and misinterpretations.mp4 111.6 MB
  • 08. Probability theory/14. Sampling variability, noise, and other annoyances.mp4 111.2 MB
  • 06. Descriptive statistics/21. Code violin plots.mp4 110.1 MB
  • 12. Correlation/22. Code Cosine similarity vs. Pearson correlation.mp4 107.2 MB
  • 16. Clustering and dimension-reduction/5. Clustering via dbscan.mp4 105.2 MB
  • 05. Visualizing data/2. Code bar plots.mp4 104.9 MB
  • 06. Descriptive statistics/24. Code entropy.mp4 101.5 MB
  • 18. A real-world data journey/4. MATLAB Import the divorce data.mp4 101.0 MB
  • 08. Probability theory/10. Code cdfs and pdfs.mp4 100.6 MB
  • 11. Confidence intervals on parameters/3. Code compute confidence intervals by formula.mp4 98.9 MB
  • 10. The t-test family/5. Two-samples t-test.mp4 98.4 MB
  • 08. Probability theory/23. Code the CLT in action.mp4 97.9 MB
  • 09. Hypothesis testing/1. IVs, DVs, models, and other stats lingo.mp4 95.6 MB
  • 06. Descriptive statistics/16. Code QQ plots.mp4 94.7 MB
  • 09. Hypothesis testing/8. Parametric vs. non-parametric tests.mp4 91.7 MB
  • 08. Probability theory/17. Conditional probability.mp4 89.8 MB
  • 05. Visualizing data/4. Code box plots.mp4 87.7 MB
  • 06. Descriptive statistics/14. Code IQR.mp4 87.4 MB
  • 14. Regression/14. Code Logistic regression.mp4 85.2 MB
  • 05. Visualizing data/10. Code pie charts.mp4 82.8 MB
  • 09. Hypothesis testing/9. Multiple comparisons and Bonferroni correction.mp4 79.1 MB
  • 14. Regression/8. Standardizing regression coefficients.mp4 78.8 MB
  • 13. Analysis of Variance (ANOVA)/2. ANOVA intro, part 2.mp4 77.5 MB
  • 16. Clustering and dimension-reduction/14. Code ICA.mp4 76.9 MB
  • 13. Analysis of Variance (ANOVA)/9. Code One-way repeated-measures ANOVA.mp4 76.7 MB
  • 12. Correlation/4. Code Simulate data with specified correlation.mp4 73.5 MB
  • 17. Signal detection theory/3. Code d-prime.mp4 72.9 MB
  • 07. Data normalizations and outliers/3. Code z-score.mp4 70.0 MB
  • 06. Descriptive statistics/9. Code computing central tendency.mp4 69.8 MB
  • 08. Probability theory/8. Code compute probability mass functions.mp4 69.4 MB
  • 07. Data normalizations and outliers/15. Code Data trimming to remove outliers.mp4 68.5 MB
  • 17. Signal detection theory/7. Receiver operating characteristics (ROC).mp4 67.5 MB
  • 10. The t-test family/12. Permutation testing for t-test significance.mp4 66.6 MB
  • 13. Analysis of Variance (ANOVA)/5. The omnibus F-test and post-hoc comparisons.mp4 66.4 MB
  • 14. Regression/3. Evaluating regression models R2 and F.mp4 65.8 MB
  • 14. Regression/1. Introduction to GLM regression.mp4 65.0 MB
  • 17. Signal detection theory/2. d-prime.mp4 62.5 MB
  • 08. Probability theory/16. Expected value.mp4 62.5 MB
  • 04. What are (is) data/3. Types of data categorical, numerical, etc.mp4 62.3 MB
  • 12. Correlation/9. Partial correlation.mp4 62.2 MB
  • 06. Descriptive statistics/11. Measures of dispersion (variance, standard deviation).mp4 59.7 MB
  • 17. Signal detection theory/8. Code ROC curves.mp4 57.3 MB
  • 16. Clustering and dimension-reduction/1. K-means clustering.mp4 56.9 MB
  • 11. Confidence intervals on parameters/4. Confidence intervals via bootstrapping (resampling).mp4 56.9 MB
  • 10. The t-test family/2. One-sample t-test.mp4 56.6 MB
  • 18. A real-world data journey/2. Introduction.mp4 55.6 MB
  • 14. Regression/13. Logistic regression.mp4 55.3 MB
  • 14. Regression/5. Code simple regression.mp4 54.8 MB
  • 10. The t-test family/11. Code Mann-Whitney U test.mp4 54.6 MB
  • 03. IMPORTANT Download course materials/1. Download materials for the entire course!.mp4 53.7 MB
  • 09. Hypothesis testing/2. What is an hypothesis and how do you specify one.mp4 51.5 MB
  • 01. Introductions/3. Statistics guessing game!.mp4 50.7 MB
  • 14. Regression/10. Polynomial regression models.mp4 50.5 MB
  • 04. What are (is) data/4. Code representing types of data on computers.mp4 50.2 MB
  • 09. Hypothesis testing/7. Type 1 and Type 2 errors.mp4 48.1 MB
  • 13. Analysis of Variance (ANOVA)/3. Sum of squares.mp4 48.1 MB
  • 16. Clustering and dimension-reduction/13. Independent components analysis (ICA).mp4 47.7 MB
  • 08. Probability theory/9. Cumulative distribution functions.mp4 47.6 MB
  • 14. Regression/7. Multiple regression.mp4 47.3 MB
  • 01. Introductions/1. Important Getting the most out of this course.mp4 47.0 MB
  • 13. Analysis of Variance (ANOVA)/7. One-way ANOVA example.mp4 46.5 MB
  • 18. A real-world data journey/10. Take-home messages.mp4 45.9 MB
  • 09. Hypothesis testing/3. Sample distributions under null and alternative hypotheses.mp4 45.9 MB
  • 05. Visualizing data/6. Histograms.mp4 45.9 MB
  • 07. Data normalizations and outliers/13. Code Euclidean distance for outlier removal.mp4 45.8 MB
  • 07. Data normalizations and outliers/7. What are outliers and why are they dangerous.mp4 45.1 MB
  • 12. Correlation/14. Code Spearman correlation and Fisher-Z.mp4 44.8 MB
  • 16. Clustering and dimension-reduction/10. Principal components analysis (PCA).mp4 44.6 MB
  • 09. Hypothesis testing/12. Statistical significance vs. classification accuracy.mp4 44.6 MB
  • 14. Regression/2. Least-squares solution to the GLM.mp4 43.4 MB
  • 14. Regression/17. Comparing nested models.mp4 43.3 MB
  • 08. Probability theory/1. What is probability.mp4 43.1 MB
  • 08. Probability theory/20. The Law of Large Numbers.mp4 42.5 MB
  • 07. Data normalizations and outliers/5. Code min-max scaling.mp4 42.4 MB
  • 15. Statistical power and sample sizes/1. What is statistical power and why is it important.mp4 42.2 MB
  • 12. Correlation/2. Covariance and correlation formulas.mp4 41.9 MB
  • 06. Descriptive statistics/7. Measures of central tendency (mean).mp4 40.6 MB
  • 08. Probability theory/3. Computing probabilities.mp4 39.3 MB
  • 08. Probability theory/2. Probability vs. proportion.mp4 39.3 MB
  • 05. Visualizing data/13. Code line plots.mp4 39.1 MB
  • 04. What are (is) data/5. Sample vs. population data.mp4 38.9 MB
  • 05. Visualizing data/1. Bar plots.mp4 38.6 MB
  • 14. Regression/4. Simple regression.mp4 38.6 MB
  • 07. Data normalizations and outliers/2. Z-score standardization.mp4 38.0 MB
  • 15. Statistical power and sample sizes/2. Estimating statistical power and sample size.mp4 37.8 MB
  • 13. Analysis of Variance (ANOVA)/10. Two-way ANOVA example.mp4 37.7 MB
  • 04. What are (is) data/2. Where do data come from and what do they mean.mp4 37.3 MB
  • 18. A real-world data journey/5. MATLAB More data visualizations.mp4 36.0 MB
  • 06. Descriptive statistics/8. Measures of central tendency (median, mode).mp4 35.9 MB
  • 07. Data normalizations and outliers/17. Nonlinear data transformations.mp4 35.3 MB
  • 07. Data normalizations and outliers/8. Removing outliers z-score method.mp4 35.1 MB
  • 06. Descriptive statistics/23. Shannon entropy.mp4 34.7 MB
  • 09. Hypothesis testing/6. Degrees of freedom.mp4 34.5 MB
  • 10. The t-test family/14. Unsupervised learning How many permutations.mp4 34.1 MB
  • 12. Correlation/17. Kendall's correlation for ordinal data.mp4 34.0 MB
  • 10. The t-test family/1. Purpose and interpretation of the t-test.mp4 33.7 MB
  • 06. Descriptive statistics/3. Data distributions.mp4 33.5 MB
  • 15. Statistical power and sample sizes/3. Compute power and sample size using GPower.mp4 32.7 MB
  • 12. Correlation/5. Correlation matrix.mp4 32.5 MB
  • 11. Confidence intervals on parameters/1. What are confidence intervals and why do we need them.mp4 31.3 MB
  • 10. The t-test family/4. Unsupervised learning The role of variance.mp4 30.0 MB
  • 12. Correlation/13. Fisher-Z transformation for correlations.mp4 29.9 MB
  • 02. Math prerequisites/1. Should you memorize statistical formulas.mp4 29.4 MB
  • 09. Hypothesis testing/11. Cross-validation.mp4 29.3 MB
  • 08. Probability theory/22. The Central Limit Theorem.mp4 28.0 MB
  • 10. The t-test family/8. Wilcoxon signed-rank (nonparametric t-test).mp4 27.2 MB
  • 05. Visualizing data/12. Linear vs. logarithmic axis scaling.mp4 26.9 MB
  • 06. Descriptive statistics/2. Accuracy, precision, resolution.mp4 26.7 MB
  • 07. Data normalizations and outliers/12. Multivariate outlier detection.mp4 26.3 MB
  • 01. Introductions/4. Using the Q&A forum.mp4 25.5 MB
  • 12. Correlation/12. Nonparametric correlation Spearman rank.mp4 24.9 MB
  • 06. Descriptive statistics/18. Histograms part 2 Number of bins.mp4 24.6 MB
  • 07. Data normalizations and outliers/16. Non-parametric solutions to outliers.mp4 24.1 MB
  • 17. Signal detection theory/5. Code Response bias.mp4 23.9 MB
  • 17. Signal detection theory/4. Response bias.mp4 22.9 MB
  • 06. Descriptive statistics/17. Statistical moments.mp4 22.7 MB
  • 12. Correlation/20. The subgroups correlation paradox.mp4 22.6 MB
  • 06. Descriptive statistics/1. Descriptive vs. inferential statistics.mp4 22.5 MB
  • 10. The t-test family/10. Mann-Whitney U test (nonparametric t-test).mp4 21.3 MB
  • 16. Clustering and dimension-reduction/7. Unsupervised learning dbscan vs. k-means.mp4 20.9 MB
  • 13. Analysis of Variance (ANOVA)/4. The F-test and the ANOVA table.mp4 20.9 MB
  • 04. What are (is) data/7. The ethics of making up data.mp4 20.6 MB
  • 09. Hypothesis testing/10. Statistical vs. theoretical vs. clinical significance.mp4 20.0 MB
  • 11. Confidence intervals on parameters/7. Misconceptions about confidence intervals.mp4 19.5 MB
  • 12. Correlation/7. Unsupervised learning average correlation matrices.mp4 19.4 MB
  • 05. Visualizing data/11. When to use lines instead of bars.mp4 18.9 MB
  • 02. Math prerequisites/7. The logistic function.mp4 18.8 MB
  • 04. What are (is) data/6. Samples, case reports, and anecdotes.mp4 18.7 MB
  • 07. Data normalizations and outliers/18. An outlier lecture on personal accountability.mp4 18.6 MB
  • 02. Math prerequisites/6. Natural exponent and logarithm.mp4 18.5 MB
  • 11. Confidence intervals on parameters/2. Computing confidence intervals via formula.mp4 18.2 MB
  • 06. Descriptive statistics/22. Unsupervised learning asymmetric violin plots.mp4 18.2 MB
  • 09. Hypothesis testing/5. P-z combinations that you should memorize.mp4 18.2 MB
  • 07. Data normalizations and outliers/14. Removing outliers by data trimming.mp4 17.7 MB
  • 10. The t-test family/7. Unsupervised learning Importance of N for t-test.mp4 17.6 MB
  • 06. Descriptive statistics/10. Unsupervised learning central tendencies with outliers.mp4 17.6 MB
  • 12. Correlation/11. The problem with Pearson.mp4 17.4 MB
  • 05. Visualizing data/9. Pie charts.mp4 17.3 MB
  • 06. Descriptive statistics/15. QQ plots.mp4 17.0 MB
  • 14. Regression/18. What to do about missing data.mp4 16.8 MB
  • 12. Correlation/15. Unsupervised learning Spearman correlation.mp4 16.7 MB
  • 12. Correlation/19. Unsupervised learning Does Kendall vs. Pearson matter.mp4 15.7 MB
  • 12. Correlation/21. Cosine similarity.mp4 14.9 MB
  • 17. Signal detection theory/1. The two perspectives of the world.mp4 14.6 MB
  • 02. Math prerequisites/8. Rank and tied-rank.mp4 14.3 MB
  • 08. Probability theory/19. Tree diagrams for conditional probabilities.mp4 14.2 MB
  • 16. Clustering and dimension-reduction/3. Unsupervised learning K-means and normalization.mp4 13.5 MB
  • 02. Math prerequisites/3. Scientific notation.mp4 13.5 MB
  • 16. Clustering and dimension-reduction/8. K-nearest neighbor classification.mp4 13.1 MB
  • 08. Probability theory/5. Probability and odds.mp4 12.6 MB
  • 05. Visualizing data/8. Unsupervised learning Histogram proportion.mp4 12.4 MB
  • 07. Data normalizations and outliers/4. Min-max scaling.mp4 12.3 MB
  • 07. Data normalizations and outliers/1. Garbage in, garbage out (GIGO).mp4 12.1 MB
  • 01. Introductions/2. About using MATLAB or Python.mp4 12.1 MB
  • 16. Clustering and dimension-reduction/12. Unsupervised learning K-means on PC data.mp4 12.1 MB
  • 17. Signal detection theory/9. Unsupervised learning Make this plot look nicer!.mp4 12.1 MB
  • 05. Visualizing data/3. Box-and-whisker plots.mp4 11.7 MB
  • 04. What are (is) data/1. Is data singular or plural!!!!.mp4 11.5 MB
  • 12. Correlation/16. Unsupervised learning confidence interval on correlation.mp4 10.8 MB
  • 06. Descriptive statistics/6. The beauty and simplicity of Normal.mp4 10.7 MB
  • 06. Descriptive statistics/5. Unsupervised learning histograms of distributions.mp4 10.7 MB
  • 12. Correlation/8. Unsupervised learning correlation to covariance matrix.mp4 10.6 MB
  • 06. Descriptive statistics/13. Interquartile range (IQR).mp4 10.3 MB
  • 07. Data normalizations and outliers/9. The modified z-score method.mp4 10.1 MB
  • 08. Probability theory/24. Unsupervised learning Averaging pairs of numbers.mp4 9.9 MB
  • 08. Probability theory/11. Unsupervised learning cdf's for various distributions.mp4 9.8 MB
  • 07. Data normalizations and outliers/11. Unsupervised learning z vs. modified-z.mp4 9.5 MB
  • 08. Probability theory/13. Monte Carlo sampling.mp4 9.3 MB
  • 11. Confidence intervals on parameters/6. Unsupervised learning Confidence intervals for variance.mp4 9.0 MB
  • 06. Descriptive statistics/25. Unsupervised learning entropy and number of bins.mp4 8.7 MB
  • 05. Visualizing data/5. Unsupervised learning Boxplots of normal and uniform noise.mp4 8.6 MB
  • 16. Clustering and dimension-reduction/4. Unsupervised learning K-means on a Gauss blur.mp4 8.3 MB
  • 02. Math prerequisites/4. Summation notation.mp4 8.1 MB
  • 02. Math prerequisites/2. Arithmetic and exponents.mp4 7.9 MB
  • 01. Introductions/5. (optional) Entering time-stamped notes in the Udemy video player.mp4 7.4 MB
  • 02. Math prerequisites/5. Absolute value.mp4 7.3 MB
  • 07. Data normalizations and outliers/6. Unsupervised learning Invert the min-max scaling.mp4 7.1 MB
  • 06. Descriptive statistics/20. Violin plots.mp4 6.8 MB
  • 08. Probability theory/6. Unsupervised learning probabilities of odds-space.mp4 6.2 MB
  • 14. Regression/6. Unsupervised learning Compute R2 and F.mp4 5.6 MB
  • 14. Regression/16. Unsupervised learning Overfit data.mp4 5.1 MB
  • 14. Regression/12. Unsupervised learning Polynomial design matrix.mp4 5.0 MB
  • 05. Visualizing data/14. Unsupervised learning log-scaled plots.mp4 3.9 MB
  • 03. IMPORTANT Download course materials/1. Statistics_course-main.zip 2.6 MB
  • 16. Clustering and dimension-reduction/6. Code dbscan.vtt 47.2 kB
  • 06. Descriptive statistics/4. Code data from different distributions.vtt 44.7 kB
  • 12. Correlation/3. Code correlation coefficient.vtt 38.9 kB
  • 06. Descriptive statistics/12. Code Computing dispersion.vtt 37.0 kB
  • 08. Probability theory/15. Code sampling variability.vtt 36.6 kB
  • 10. The t-test family/13. Code permutation testing.vtt 35.8 kB
  • 17. Signal detection theory/6. F-score.vtt 33.9 kB
  • 16. Clustering and dimension-reduction/2. Code k-means clustering.vtt 33.3 kB
  • 07. Data normalizations and outliers/10. Code z-score for outlier removal.vtt 32.5 kB
  • 10. The t-test family/6. Code Two-samples t-test.vtt 31.0 kB
  • 18. A real-world data journey/7. Python Import and clean the marriage data.vtt 30.2 kB
  • 12. Correlation/22. Code Cosine similarity vs. Pearson correlation.vtt 30.2 kB
  • 13. Analysis of Variance (ANOVA)/6. The two-way ANOVA.vtt 30.1 kB
  • 12. Correlation/6. Code correlation matrix.vtt 30.0 kB
  • 14. Regression/1. Introduction to GLM regression.vtt 29.6 kB
  • 10. The t-test family/3. Code One-sample t-test.vtt 29.4 kB
  • 08. Probability theory/18. Code conditional probabilities.vtt 28.4 kB
  • 06. Descriptive statistics/24. Code entropy.vtt 28.3 kB
  • 13. Analysis of Variance (ANOVA)/2. ANOVA intro, part 2.vtt 28.3 kB
  • 12. Correlation/10. Code partial correlation.vtt 28.2 kB
  • 08. Probability theory/12. Creating sample estimate distributions.vtt 27.5 kB
  • 14. Regression/9. Code Multiple regression.vtt 27.2 kB
  • 12. Correlation/1. Motivation and description of correlation.vtt 27.0 kB
  • 08. Probability theory/21. Code Law of Large Numbers in action.vtt 26.6 kB
  • 06. Descriptive statistics/11. Measures of dispersion (variance, standard deviation).vtt 26.4 kB
  • 09. Hypothesis testing/4. P-values definition, tails, and misinterpretations.vtt 26.3 kB
  • 13. Analysis of Variance (ANOVA)/3. Sum of squares.vtt 26.3 kB
  • 13. Analysis of Variance (ANOVA)/1. ANOVA intro, part1.vtt 26.0 kB
  • 10. The t-test family/9. Code Signed-rank test.vtt 25.6 kB
  • 12. Correlation/18. Code Kendall correlation.vtt 25.3 kB
  • 16. Clustering and dimension-reduction/11. Code PCA.vtt 25.1 kB
  • 14. Regression/15. Under- and over-fitting.vtt 24.4 kB
  • 16. Clustering and dimension-reduction/10. Principal components analysis (PCA).vtt 24.4 kB
  • 18. A real-world data journey/3. MATLAB Import and clean the marriage data.vtt 24.3 kB
  • 18. A real-world data journey/3. state-marriage-rates-90-95-99-19.xlsx 24.2 kB
  • 09. Hypothesis testing/1. IVs, DVs, models, and other stats lingo.vtt 24.1 kB
  • 13. Analysis of Variance (ANOVA)/8. Code One-way ANOVA (independent samples).vtt 24.1 kB
  • 14. Regression/13. Logistic regression.vtt 24.1 kB
  • 11. Confidence intervals on parameters/3. Code compute confidence intervals by formula.vtt 23.9 kB
  • 05. Visualizing data/7. Code histograms.vtt 23.7 kB
  • 05. Visualizing data/2. Code bar plots.vtt 23.2 kB
  • 08. Probability theory/23. Code the CLT in action.vtt 23.2 kB
  • 18. A real-world data journey/4. state-divorce-rates-90-95-99-19.xlsx 23.0 kB
  • 06. Descriptive statistics/16. Code QQ plots.vtt 22.8 kB
  • 06. Descriptive statistics/14. Code IQR.vtt 22.0 kB
  • 14. Regression/11. Code polynomial modeling.vtt 21.9 kB
  • 09. Hypothesis testing/2. What is an hypothesis and how do you specify one.vtt 21.6 kB
  • 08. Probability theory/4. Code compute probabilities.vtt 21.4 kB
  • 16. Clustering and dimension-reduction/5. Clustering via dbscan.vtt 21.3 kB
  • 09. Hypothesis testing/7. Type 1 and Type 2 errors.vtt 21.3 kB
  • 07. Data normalizations and outliers/7. What are outliers and why are they dangerous.vtt 21.3 kB
  • 07. Data normalizations and outliers/17. Nonlinear data transformations.vtt 21.0 kB
  • 17. Signal detection theory/3. Code d-prime.vtt 20.9 kB
  • 08. Probability theory/9. Cumulative distribution functions.vtt 20.8 kB
  • 16. Clustering and dimension-reduction/1. K-means clustering.vtt 20.8 kB
  • 04. What are (is) data/3. Types of data categorical, numerical, etc.vtt 20.8 kB
  • 13. Analysis of Variance (ANOVA)/7. One-way ANOVA example.vtt 20.4 kB
  • 11. Confidence intervals on parameters/5. Code bootstrapping confidence intervals.vtt 20.3 kB
  • 13. Analysis of Variance (ANOVA)/11. Code Two-way mixed ANOVA.vtt 20.2 kB
  • 14. Regression/3. Evaluating regression models R2 and F.vtt 20.2 kB
  • 06. Descriptive statistics/9. Code computing central tendency.vtt 19.7 kB
  • 12. Correlation/4. Code Simulate data with specified correlation.vtt 19.7 kB
  • 05. Visualizing data/10. Code pie charts.vtt 19.2 kB
  • 10. The t-test family/1. Purpose and interpretation of the t-test.vtt 19.2 kB
  • 10. The t-test family/5. Two-samples t-test.vtt 19.1 kB
  • 14. Regression/4. Simple regression.vtt 18.9 kB
  • 14. Regression/7. Multiple regression.vtt 18.8 kB
  • 09. Hypothesis testing/9. Multiple comparisons and Bonferroni correction.vtt 18.7 kB
  • 07. Data normalizations and outliers/3. Code z-score.vtt 18.6 kB
  • 13. Analysis of Variance (ANOVA)/5. The omnibus F-test and post-hoc comparisons.vtt 18.5 kB
  • 18. A real-world data journey/8. Python Import the divorce data.vtt 18.4 kB
  • 06. Descriptive statistics/7. Measures of central tendency (mean).vtt 18.3 kB
  • 17. Signal detection theory/2. d-prime.vtt 18.3 kB
  • 08. Probability theory/7. Probability mass vs. density.vtt 18.2 kB
  • 08. Probability theory/17. Conditional probability.vtt 18.2 kB
  • 13. Analysis of Variance (ANOVA)/9. Code One-way repeated-measures ANOVA.vtt 18.1 kB
  • 12. Correlation/2. Covariance and correlation formulas.vtt 18.1 kB
  • 06. Descriptive statistics/19. Code Histogram bins.vtt 18.0 kB
  • 06. Descriptive statistics/8. Measures of central tendency (median, mode).vtt 18.0 kB
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  • 18. A real-world data journey/9. Python Inferential statistics.vtt 16.7 kB
  • 09. Hypothesis testing/12. Statistical significance vs. classification accuracy.vtt 16.5 kB
  • 13. Analysis of Variance (ANOVA)/10. Two-way ANOVA example.vtt 16.5 kB
  • 15. Statistical power and sample sizes/2. Estimating statistical power and sample size.vtt 16.2 kB
  • 08. Probability theory/22. The Central Limit Theorem.vtt 15.9 kB
  • 05. Visualizing data/6. Histograms.vtt 15.9 kB
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  • 06. Descriptive statistics/23. Shannon entropy.vtt 15.6 kB
  • 12. Correlation/9. Partial correlation.vtt 15.6 kB
  • 15. Statistical power and sample sizes/1. What is statistical power and why is it important.vtt 15.3 kB
  • 18. A real-world data journey/6. MATLAB Inferential statistics.vtt 15.3 kB
  • 12. Correlation/17. Kendall's correlation for ordinal data.vtt 15.2 kB
  • 08. Probability theory/16. Expected value.vtt 15.0 kB
  • 06. Descriptive statistics/21. Code violin plots.vtt 14.8 kB
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  • 09. Hypothesis testing/8. Parametric vs. non-parametric tests.vtt 13.5 kB
  • 12. Correlation/5. Correlation matrix.vtt 13.4 kB
  • 01. Introductions/3. Statistics guessing game!.vtt 13.4 kB
  • 11. Confidence intervals on parameters/4. Confidence intervals via bootstrapping (resampling).vtt 13.2 kB
  • 14. Regression/5. Code simple regression.vtt 13.1 kB
  • 02. Math prerequisites/7. The logistic function.vtt 12.7 kB
  • 07. Data normalizations and outliers/13. Code Euclidean distance for outlier removal.vtt 12.7 kB
  • 11. Confidence intervals on parameters/1. What are confidence intervals and why do we need them.vtt 12.6 kB
  • 14. Regression/10. Polynomial regression models.vtt 12.5 kB
  • 04. What are (is) data/4. Code representing types of data on computers.vtt 12.4 kB
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  • 11. Confidence intervals on parameters/2. Computing confidence intervals via formula.vtt 9.6 kB
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  • 06. Descriptive statistics/6. The beauty and simplicity of Normal.vtt 8.2 kB
  • 01. Introductions/4. Using the Q&A forum.vtt 8.1 kB
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  • 10. The t-test family/11. Code Mann-Whitney U test.vtt 7.6 kB
  • 03. IMPORTANT Download course materials/1. Download materials for the entire course!.vtt 7.2 kB
  • 07. Data normalizations and outliers/4. Min-max scaling.vtt 7.2 kB
  • 12. Correlation/20. The subgroups correlation paradox.vtt 7.2 kB
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  • 10. The t-test family/7. Unsupervised learning Importance of N for t-test.vtt 6.8 kB
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  • 01. Introductions/1. Important Getting the most out of this course.vtt 6.5 kB
  • 06. Descriptive statistics/1. Descriptive vs. inferential statistics.vtt 6.4 kB
  • 17. Signal detection theory/5. Code Response bias.vtt 6.3 kB
  • 15. Statistical power and sample sizes/3. Compute power and sample size using GPower.vtt 6.2 kB
  • 18. A real-world data journey/2. Introduction.vtt 6.2 kB
  • 07. Data normalizations and outliers/9. The modified z-score method.vtt 6.0 kB
  • 12. Correlation/8. Unsupervised learning correlation to covariance matrix.vtt 6.0 kB
  • 01. Introductions/2. About using MATLAB or Python.vtt 6.0 kB
  • 02. Math prerequisites/4. Summation notation.vtt 6.0 kB
  • 07. Data normalizations and outliers/1. Garbage in, garbage out (GIGO).vtt 5.9 kB
  • 02. Math prerequisites/2. Arithmetic and exponents.vtt 5.7 kB
  • 19. Bonus section/2. Bonus content.html 4.8 kB
  • 06. Descriptive statistics/20. Violin plots.vtt 4.8 kB
  • 16. Clustering and dimension-reduction/7. Unsupervised learning dbscan vs. k-means.vtt 4.5 kB
  • 07. Data normalizations and outliers/18. An outlier lecture on personal accountability.vtt 4.4 kB
  • 06. Descriptive statistics/10. Unsupervised learning central tendencies with outliers.vtt 4.4 kB
  • 02. Math prerequisites/1. Should you memorize statistical formulas.vtt 4.4 kB
  • 02. Math prerequisites/5. Absolute value.vtt 4.3 kB
  • 10. The t-test family/4. Unsupervised learning The role of variance.vtt 4.2 kB
  • 12. Correlation/7. Unsupervised learning average correlation matrices.vtt 4.2 kB
  • 08. Probability theory/13. Monte Carlo sampling.vtt 4.0 kB
  • 07. Data normalizations and outliers/11. Unsupervised learning z vs. modified-z.vtt 4.0 kB
  • 05. Visualizing data/5. Unsupervised learning Boxplots of normal and uniform noise.vtt 3.8 kB
  • 01. Introductions/3. stats_intro_GuessTheTest.zip 3.8 kB
  • 07. Data normalizations and outliers/6. Unsupervised learning Invert the min-max scaling.vtt 3.8 kB
  • 06. Descriptive statistics/22. Unsupervised learning asymmetric violin plots.vtt 3.7 kB
  • 12. Correlation/19. Unsupervised learning Does Kendall vs. Pearson matter.vtt 3.7 kB
  • 12. Correlation/16. Unsupervised learning confidence interval on correlation.vtt 3.5 kB
  • 08. Probability theory/11. Unsupervised learning cdf's for various distributions.vtt 3.5 kB
  • 05. Visualizing data/8. Unsupervised learning Histogram proportion.vtt 3.5 kB
  • 08. Probability theory/6. Unsupervised learning probabilities of odds-space.vtt 3.2 kB
  • 08. Probability theory/24. Unsupervised learning Averaging pairs of numbers.vtt 3.2 kB
  • 01. Introductions/5. (optional) Entering time-stamped notes in the Udemy video player.vtt 3.1 kB
  • 06. Descriptive statistics/5. Unsupervised learning histograms of distributions.vtt 2.9 kB
  • 14. Regression/16. Unsupervised learning Overfit data.vtt 2.8 kB
  • 16. Clustering and dimension-reduction/3. Unsupervised learning K-means and normalization.vtt 2.7 kB
  • 04. What are (is) data/1. Is data singular or plural!!!!.vtt 2.5 kB
  • 05. Visualizing data/14. Unsupervised learning log-scaled plots.vtt 2.5 kB
  • 17. Signal detection theory/9. Unsupervised learning Make this plot look nicer!.vtt 2.5 kB
  • 16. Clustering and dimension-reduction/12. Unsupervised learning K-means on PC data.vtt 2.3 kB
  • 12. Correlation/15. Unsupervised learning Spearman correlation.vtt 2.1 kB
  • 06. Descriptive statistics/25. Unsupervised learning entropy and number of bins.vtt 2.1 kB
  • 11. Confidence intervals on parameters/6. Unsupervised learning Confidence intervals for variance.vtt 2.1 kB
  • 16. Clustering and dimension-reduction/4. Unsupervised learning K-means on a Gauss blur.vtt 2.1 kB
  • 14. Regression/6. Unsupervised learning Compute R2 and F.vtt 1.5 kB
  • 14. Regression/12. Unsupervised learning Polynomial design matrix.vtt 1.2 kB
  • 19. Bonus section/1. About deep learning.html 658 Bytes
  • 18. A real-world data journey/1. Note about the code for this section.html 174 Bytes
  • 03. IMPORTANT Download course materials/1. Link-to-code-on-github.txt 47 Bytes

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