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GetFreeCourses.Co-Udemy-Master statistics & machine learning - intuition, math, code

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GetFreeCourses Co-Udemy-Master statistics & machine learning - intuition math code

文件列表
  1. 01 - Introductions/001 [Important] Getting the most out of this course.mp438.26MB
  2. 01 - Introductions/002 About using MATLAB or Python.mp427.11MB
  3. 01 - Introductions/003 Statistics guessing game_.mp448.39MB
  4. 01 - Introductions/004 Using the Q&A forum.mp424.36MB
  5. 01 - Introductions/005 (optional) Entering time-stamped notes in the Udemy video player.mp47.06MB
  6. 02 - Math prerequisites/001 Should you memorize statistical formulas_.mp428MB
  7. 02 - Math prerequisites/002 Arithmetic and exponents.mp47.55MB
  8. 02 - Math prerequisites/003 Scientific notation.mp412.87MB
  9. 02 - Math prerequisites/004 Summation notation.mp47.73MB
  10. 02 - Math prerequisites/005 Absolute value.mp46.92MB
  11. 02 - Math prerequisites/006 Natural exponent and logarithm.mp412.18MB
  12. 02 - Math prerequisites/007 The logistic function.mp417.9MB
  13. 02 - Math prerequisites/008 Rank and tied-rank.mp412.92MB
  14. 03 - IMPORTANT_ Download course materials/001 Download materials for the entire course_.mp414.46MB
  15. 04 - What are (is_) data_/001 Is _data_ singular or plural_______.mp410.92MB
  16. 04 - What are (is_) data_/002 Where do data come from and what do they mean_.mp435.54MB
  17. 04 - What are (is_) data_/003 Types of data_ categorical, numerical, etc.mp459.37MB
  18. 04 - What are (is_) data_/004 Code_ representing types of data on computers.mp447.83MB
  19. 04 - What are (is_) data_/005 Sample vs. population data.mp437.06MB
  20. 04 - What are (is_) data_/006 Samples, case reports, and anecdotes.mp417.79MB
  21. 04 - What are (is_) data_/007 The ethics of making up data.mp419.65MB
  22. 05 - Visualizing data/001 Bar plots.mp436.83MB
  23. 05 - Visualizing data/002 Code_ bar plots.mp4100.03MB
  24. 05 - Visualizing data/003 Box-and-whisker plots.mp411.12MB
  25. 05 - Visualizing data/004 Code_ box plots.mp483.65MB
  26. 05 - Visualizing data/005 _Unsupervised learning__ Boxplots of normal and uniform noise.mp48.24MB
  27. 05 - Visualizing data/006 Histograms.mp443.73MB
  28. 05 - Visualizing data/007 Code_ histograms.mp4133.49MB
  29. 05 - Visualizing data/008 _Unsupervised learning__ Histogram proportion.mp411.79MB
  30. 05 - Visualizing data/009 Pie charts.mp416.53MB
  31. 05 - Visualizing data/010 Code_ pie charts.mp478.92MB
  32. 05 - Visualizing data/011 When to use lines instead of bars.mp417.98MB
  33. 05 - Visualizing data/012 Linear vs. logarithmic axis scaling.mp425.64MB
  34. 05 - Visualizing data/013 Code_ line plots.mp437.29MB
  35. 05 - Visualizing data/014 _Unsupervised learning__ log-scaled plots.mp43.73MB
  36. 06 - Descriptive statistics/001 Descriptive vs. inferential statistics.mp421.48MB
  37. 06 - Descriptive statistics/002 Accuracy, precision, resolution.mp425.42MB
  38. 06 - Descriptive statistics/003 Data distributions.mp431.95MB
  39. 06 - Descriptive statistics/004 Code_ data from different distributions.mp4303.11MB
  40. 06 - Descriptive statistics/005 _Unsupervised learning__ histograms of distributions.mp410.18MB
  41. 06 - Descriptive statistics/006 The beauty and simplicity of Normal.mp410.23MB
  42. 06 - Descriptive statistics/007 Measures of central tendency (mean).mp438.7MB
  43. 06 - Descriptive statistics/008 Measures of central tendency (median, mode).mp434.26MB
  44. 06 - Descriptive statistics/009 Code_ computing central tendency.mp466.6MB
  45. 06 - Descriptive statistics/010 _Unsupervised learning__ central tendencies with outliers.mp416.74MB
  46. 06 - Descriptive statistics/011 Measures of dispersion (variance, standard deviation).mp454.12MB
  47. 06 - Descriptive statistics/012 Code_ Computing dispersion.mp4266.09MB
  48. 06 - Descriptive statistics/013 Interquartile range (IQR).mp49.84MB
  49. 06 - Descriptive statistics/014 Code_ IQR.mp483.39MB
  50. 06 - Descriptive statistics/015 QQ plots.mp416.22MB
  51. 06 - Descriptive statistics/016 Code_ QQ plots.mp490.3MB
  52. 06 - Descriptive statistics/017 Statistical _moments_.mp421.68MB
  53. 06 - Descriptive statistics/018 Histograms part 2_ Number of bins.mp423.5MB
  54. 06 - Descriptive statistics/019 Code_ Histogram bins.mp4118.12MB
  55. 06 - Descriptive statistics/020 Violin plots.mp46.47MB
  56. 06 - Descriptive statistics/021 Code_ violin plots.mp4104.96MB
  57. 06 - Descriptive statistics/022 _Unsupervised learning__ asymmetric violin plots.mp417.32MB
  58. 06 - Descriptive statistics/023 Shannon entropy.mp433.05MB
  59. 06 - Descriptive statistics/024 Code_ entropy.mp496.76MB
  60. 06 - Descriptive statistics/025 _Unsupervised learning__ entropy and number of bins.mp48.25MB
  61. 07 - Data normalizations and outliers/001 Garbage in, garbage out (GIGO).mp411.55MB
  62. 07 - Data normalizations and outliers/002 Z-score standardization.mp436.23MB
  63. 07 - Data normalizations and outliers/003 Code_ z-score.mp466.77MB
  64. 07 - Data normalizations and outliers/004 Min-max scaling.mp411.73MB
  65. 07 - Data normalizations and outliers/005 Code_ min-max scaling.mp440.43MB
  66. 07 - Data normalizations and outliers/006 _Unsupervised learning__ Invert the min-max scaling.mp46.79MB
  67. 07 - Data normalizations and outliers/007 What are outliers and why are they dangerous_.mp443MB
  68. 07 - Data normalizations and outliers/008 Removing outliers_ z-score method.mp433.51MB
  69. 07 - Data normalizations and outliers/009 The modified z-score method.mp49.62MB
  70. 07 - Data normalizations and outliers/010 Code_ z-score for outlier removal.mp4136.89MB
  71. 07 - Data normalizations and outliers/011 _Unsupervised learning__ z vs. modified-z.mp49.02MB
  72. 07 - Data normalizations and outliers/012 Multivariate outlier detection.mp425.05MB
  73. 07 - Data normalizations and outliers/013 Code_ Euclidean distance for outlier removal.mp443.72MB
  74. 07 - Data normalizations and outliers/014 Removing outliers by data trimming.mp416.9MB
  75. 07 - Data normalizations and outliers/015 Code_ Data trimming to remove outliers.mp465.29MB
  76. 07 - Data normalizations and outliers/016 Non-parametric solutions to outliers.mp422.96MB
  77. 07 - Data normalizations and outliers/017 Nonlinear data transformations.mp433.69MB
  78. 07 - Data normalizations and outliers/018 An outlier lecture on personal accountability.mp417.7MB
  79. 08 - Probability theory/001 What is probability_.mp441.11MB
  80. 08 - Probability theory/002 Probability vs. proportion.mp437.52MB
  81. 08 - Probability theory/003 Computing probabilities.mp437.52MB
  82. 08 - Probability theory/004 Code_ compute probabilities.mp4148.4MB
  83. 08 - Probability theory/005 Probability and odds.mp412.01MB
  84. 08 - Probability theory/006 _Unsupervised learning__ probabilities of odds-space.mp45.92MB
  85. 08 - Probability theory/007 Probability mass vs. density.mp4134.14MB
  86. 08 - Probability theory/008 Code_ compute probability mass functions.mp466.17MB
  87. 08 - Probability theory/009 Cumulative distribution functions.mp445.41MB
  88. 08 - Probability theory/010 Code_ cdfs and pdfs.mp495.94MB
  89. 08 - Probability theory/011 _Unsupervised learning__ cdf's for various distributions.mp49.31MB
  90. 08 - Probability theory/012 Creating sample estimate distributions.mp4124.85MB
  91. 08 - Probability theory/013 Monte Carlo sampling.mp48.83MB
  92. 08 - Probability theory/014 Sampling variability, noise, and other annoyances.mp4106.08MB
  93. 08 - Probability theory/015 Code_ sampling variability.mp4154.75MB
  94. 08 - Probability theory/016 Expected value.mp459.63MB
  95. 08 - Probability theory/017 Conditional probability.mp485.68MB
  96. 08 - Probability theory/018 Code_ conditional probabilities.mp4115.08MB
  97. 08 - Probability theory/019 Tree diagrams for conditional probabilities.mp413.5MB
  98. 08 - Probability theory/020 The Law of Large Numbers.mp440.55MB
  99. 08 - Probability theory/021 Code_ Law of Large Numbers in action.mp4165.6MB
  100. 08 - Probability theory/022 The Central Limit Theorem.mp426.67MB
  101. 08 - Probability theory/023 Code_ the CLT in action.mp493.32MB
  102. 08 - Probability theory/024 _Unsupervised learning__ Averaging pairs of numbers.mp49.48MB
  103. 09 - Hypothesis testing/001 IVs, DVs, models, and other stats lingo.mp491.14MB
  104. 09 - Hypothesis testing/002 What is an hypothesis and how do you specify one_.mp449.12MB
  105. 09 - Hypothesis testing/003 Sample distributions under null and alternative hypotheses.mp443.75MB
  106. 09 - Hypothesis testing/004 P-values_ definition, tails, and misinterpretations.mp4106.47MB
  107. 09 - Hypothesis testing/005 P-z combinations that you should memorize.mp417.32MB
  108. 09 - Hypothesis testing/006 Degrees of freedom.mp432.9MB
  109. 09 - Hypothesis testing/007 Type 1 and Type 2 errors.mp445.9MB
  110. 09 - Hypothesis testing/008 Parametric vs. non-parametric tests.mp487.45MB
  111. 09 - Hypothesis testing/009 Multiple comparisons and Bonferroni correction.mp429.56MB
  112. 09 - Hypothesis testing/010 Statistical vs. theoretical vs. clinical significance.mp419.08MB
  113. 09 - Hypothesis testing/011 Cross-validation.mp428.25MB
  114. 09 - Hypothesis testing/012 Statistical significance vs. classification accuracy.mp442.5MB
  115. 10 - The t-test family/001 Purpose and interpretation of the t-test.mp432.16MB
  116. 10 - The t-test family/002 One-sample t-test.mp453.95MB
  117. 10 - The t-test family/003 Code_ One-sample t-test.mp4157.96MB
  118. 10 - The t-test family/004 _Unsupervised learning__ The role of variance.mp428.65MB
  119. 10 - The t-test family/005 Two-samples t-test.mp493.81MB
  120. 10 - The t-test family/006 Code_ Two-samples t-test.mp4211.35MB
  121. 10 - The t-test family/007 _Unsupervised learning__ Importance of N for t-test.mp416.77MB
  122. 10 - The t-test family/008 Wilcoxon signed-rank (nonparametric t-test).mp425.98MB
  123. 10 - The t-test family/009 Code_ Signed-rank test.mp4161.85MB
  124. 10 - The t-test family/010 Mann-Whitney U test (nonparametric t-test).mp420.32MB
  125. 10 - The t-test family/011 Code_ Mann-Whitney U test.mp452.05MB
  126. 10 - The t-test family/012 Permutation testing for t-test significance.mp463.48MB
  127. 10 - The t-test family/013 Code_ permutation testing.mp4240.9MB
  128. 10 - The t-test family/014 _Unsupervised learning__ How many permutations_.mp432.5MB
  129. 11 - Confidence intervals on parameters/001 What are confidence intervals and why do we need them_.mp429.83MB
  130. 11 - Confidence intervals on parameters/002 Computing confidence intervals via formula.mp417.33MB
  131. 11 - Confidence intervals on parameters/003 Code_ compute confidence intervals by formula.mp494.29MB
  132. 11 - Confidence intervals on parameters/004 Confidence intervals via bootstrapping (resampling).mp454.27MB
  133. 11 - Confidence intervals on parameters/005 Code_ bootstrapping confidence intervals.mp4136.71MB
  134. 11 - Confidence intervals on parameters/006 _Unsupervised learning__ Confidence intervals for variance.mp48.54MB
  135. 11 - Confidence intervals on parameters/007 Misconceptions about confidence intervals.mp418.6MB
  136. 12 - Correlation/001 Motivation and description of correlation.mp4118.43MB
  137. 12 - Correlation/002 Covariance and correlation_ formulas.mp441.85MB
  138. 12 - Correlation/003 Code_ correlation coefficient.mp4214.14MB
  139. 12 - Correlation/004 Code_ Simulate data with specified correlation.mp470.12MB
  140. 12 - Correlation/005 Correlation matrix.mp430.96MB
  141. 12 - Correlation/006 Code_ correlation matrix.mp4282.48MB
  142. 12 - Correlation/007 _Unsupervised learning__ average correlation matrices.mp418.49MB
  143. 12 - Correlation/008 _Unsupervised learning__ correlation to covariance matrix.mp410.13MB
  144. 12 - Correlation/009 Partial correlation.mp459.34MB
  145. 12 - Correlation/010 Code_ partial correlation.mp4108.27MB
  146. 12 - Correlation/011 The problem with Pearson.mp416.57MB
  147. 12 - Correlation/012 Nonparametric correlation_ Spearman rank.mp423.72MB
  148. 12 - Correlation/013 Fisher-Z transformation for correlations.mp428.48MB
  149. 12 - Correlation/014 Code_ Spearman correlation and Fisher-Z.mp442.71MB
  150. 12 - Correlation/015 _Unsupervised learning__ Spearman correlation.mp415.95MB
  151. 12 - Correlation/016 _Unsupervised learning__ confidence interval on correlation.mp410.31MB
  152. 12 - Correlation/017 Kendall's correlation for ordinal data.mp430.15MB
  153. 12 - Correlation/018 Code_ Kendall correlation.mp4184.22MB
  154. 12 - Correlation/019 _Unsupervised learning__ Does Kendall vs. Pearson matter_.mp414.95MB
  155. 12 - Correlation/020 The subgroups correlation paradox.mp421.57MB
  156. 12 - Correlation/021 Cosine similarity.mp414.2MB
  157. 12 - Correlation/022 Code_ Cosine similarity vs. Pearson correlation.mp4102.19MB
  158. 13 - Analysis of Variance (ANOVA)/001 ANOVA intro, part1.mp4137.72MB
  159. 13 - Analysis of Variance (ANOVA)/002 ANOVA intro, part 2.mp484.25MB
  160. 13 - Analysis of Variance (ANOVA)/003 Sum of squares.mp445.88MB
  161. 13 - Analysis of Variance (ANOVA)/004 The F-test and the ANOVA table.mp419.9MB
  162. 13 - Analysis of Variance (ANOVA)/005 The omnibus F-test and post-hoc comparisons.mp463.36MB
  163. 13 - Analysis of Variance (ANOVA)/006 The two-way ANOVA.mp4104.39MB
  164. 13 - Analysis of Variance (ANOVA)/007 One-way ANOVA example.mp444.32MB
  165. 13 - Analysis of Variance (ANOVA)/008 Code_ One-way ANOVA (independent samples).mp4172.72MB
  166. 13 - Analysis of Variance (ANOVA)/009 Code_ One-way repeated-measures ANOVA.mp473.1MB
  167. 13 - Analysis of Variance (ANOVA)/010 Two-way ANOVA example.mp435.95MB
  168. 13 - Analysis of Variance (ANOVA)/011 Code_ Two-way mixed ANOVA.mp4114.16MB
  169. 14 - Regression/001 Introduction to GLM _ regression.mp461.97MB
  170. 14 - Regression/002 Least-squares solution to the GLM.mp441.41MB
  171. 14 - Regression/003 Evaluating regression models_ R2 and F.mp438.06MB
  172. 14 - Regression/004 Simple regression.mp436.77MB
  173. 14 - Regression/005 Code_ simple regression.mp452.29MB
  174. 14 - Regression/006 _Unsupervised learning__ Compute R2 and F.mp45.38MB
  175. 14 - Regression/007 Multiple regression.mp445.14MB
  176. 14 - Regression/008 Standardizing regression coefficients.mp475.19MB
  177. 14 - Regression/009 Code_ Multiple regression.mp4170.95MB
  178. 14 - Regression/010 Polynomial regression models.mp448.15MB
  179. 14 - Regression/011 Code_ polynomial modeling.mp4129.08MB
  180. 14 - Regression/012 _Unsupervised learning__ Polynomial design matrix.mp44.74MB
  181. 14 - Regression/013 Logistic regression.mp452.7MB
  182. 14 - Regression/014 Code_ Logistic regression.mp481.23MB
  183. 14 - Regression/015 Under- and over-fitting.mp4120.86MB
  184. 14 - Regression/016 _Unsupervised learning__ Overfit data.mp44.82MB
  185. 14 - Regression/017 Comparing _nested_ models.mp439.07MB
  186. 14 - Regression/018 What to do about missing data.mp416.05MB
  187. 15 - Statistical power and sample sizes/001 What is statistical power and why is it important_.mp439.53MB
  188. 15 - Statistical power and sample sizes/002 Estimating statistical power and sample size.mp436.16MB
  189. 15 - Statistical power and sample sizes/003 Compute power and sample size using G_Power.mp431.2MB
  190. 16 - Clustering and dimension-reduction/001 K-means clustering.mp454.29MB
  191. 16 - Clustering and dimension-reduction/002 Code_ k-means clustering.mp4230.34MB
  192. 16 - Clustering and dimension-reduction/003 _Unsupervised learning__ K-means and normalization.mp412.91MB
  193. 16 - Clustering and dimension-reduction/004 _Unsupervised learning__ K-means on a Gauss blur.mp47.94MB
  194. 16 - Clustering and dimension-reduction/005 Clustering via dbscan.mp4100.3MB
  195. 16 - Clustering and dimension-reduction/006 Code_ dbscan.mp4288.12MB
  196. 16 - Clustering and dimension-reduction/007 _Unsupervised learning__ dbscan vs. k-means.mp419.94MB
  197. 16 - Clustering and dimension-reduction/008 K-nearest neighbor classification.mp412.47MB
  198. 16 - Clustering and dimension-reduction/009 Code_ KNN.mp4108.34MB
  199. 16 - Clustering and dimension-reduction/010 Principal components analysis (PCA).mp442.56MB
  200. 16 - Clustering and dimension-reduction/011 Code_ PCA.mp4175.1MB
  201. 16 - Clustering and dimension-reduction/012 _Unsupervised learning__ K-means on PC data.mp411.52MB
  202. 16 - Clustering and dimension-reduction/013 Independent components analysis (ICA).mp445.52MB
  203. 16 - Clustering and dimension-reduction/014 Code_ ICA.mp473.36MB
  204. 17 - Signal detection theory/001 The two perspectives of the world.mp413.91MB
  205. 17 - Signal detection theory/002 d-prime.mp434.14MB
  206. 17 - Signal detection theory/003 Code_ d-prime.mp469.5MB
  207. 17 - Signal detection theory/004 Response bias.mp421.82MB
  208. 17 - Signal detection theory/005 Code_ Response bias.mp422.81MB
  209. 17 - Signal detection theory/006 F-score.mp4107.25MB
  210. 17 - Signal detection theory/007 Receiver operating characteristics (ROC).mp464.37MB
  211. 17 - Signal detection theory/008 Code_ ROC curves.mp454.62MB
  212. 17 - Signal detection theory/009 _Unsupervised learning__ Make this plot look nicer_.mp411.5MB
  213. 18 - A real-world data journey/002 Introduction.mp453.02MB
  214. 18 - A real-world data journey/003 MATLAB_ Import and clean the marriage data.mp4201.29MB
  215. 18 - A real-world data journey/004 MATLAB_ Import the divorce data.mp496.29MB
  216. 18 - A real-world data journey/005 MATLAB_ More data visualizations.mp434.32MB
  217. 18 - A real-world data journey/006 MATLAB_ Inferential statistics.mp4113.52MB
  218. 18 - A real-world data journey/007 Python_ Import and clean the marriage data.mp4249.82MB
  219. 18 - A real-world data journey/008 Python_ Import the divorce data.mp4137.14MB
  220. 18 - A real-world data journey/009 Python_ Inferential statistics.mp4115.54MB
  221. 18 - A real-world data journey/010 Take-home messages.mp443.8MB
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