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StatQuest

StatQuest with Josh Starmer via YouTube Direct link

Covariance, Clearly Explained!!!

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Covariance, Clearly Explained!!!

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Classroom Contents

StatQuest

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  1. 1 StatQuest: Principal Component Analysis (PCA), Step-by-Step
  2. 2 StatQuest: Logistic Regression
  3. 3 Probability is not Likelihood. Find out why!!!
  4. 4 Maximum Likelihood, clearly explained!!!
  5. 5 StatQuest: P Values, clearly explained
  6. 6 StatQuest: PCA main ideas in only 5 minutes!!!
  7. 7 StatQuest: Decision Trees
  8. 8 Machine Learning Fundamentals: Bias and Variance
  9. 9 StatQuest: K-means clustering
  10. 10 StatQuest: Random Forests Part 1 - Building, Using and Evaluating
  11. 11 ROC and AUC, Clearly Explained!
  12. 12 Regularization Part 1: Ridge (L2) Regression
  13. 13 Support Vector Machines Part 1 (of 3): Main Ideas!!!
  14. 14 Gradient Descent, Step-by-Step
  15. 15 Logistic Regression Details Pt1: Coefficients
  16. 16 StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
  17. 17 Machine Learning Fundamentals: Cross Validation
  18. 18 Linear Regression, Clearly Explained!!!
  19. 19 A Gentle Introduction to Machine Learning
  20. 20 AdaBoost, Clearly Explained
  21. 21 StatQuest: A gentle introduction to RNA-seq
  22. 22 Gradient Boost Part 1 (of 4): Regression Main Ideas
  23. 23 Maximum Likelihood For the Normal Distribution, step-by-step!!!
  24. 24 Logistic Regression in R, Clearly Explained!!!!
  25. 25 Quantile-Quantile Plots (QQ plots), Clearly Explained!!!
  26. 26 p-values: What they are and how to interpret them
  27. 27 StatQuest: t-SNE, Clearly Explained
  28. 28 Regularization Part 2: Lasso (L1) Regression
  29. 29 Machine Learning Fundamentals: The Confusion Matrix
  30. 30 R-squared, Clearly Explained!!!
  31. 31 The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
  32. 32 Covariance, Clearly Explained!!!
  33. 33 Logistic Regression Details Pt 2: Maximum Likelihood
  34. 34 The Normal Distribution, Clearly Explained!!!
  35. 35 StatQuest: Histograms, Clearly Explained
  36. 36 StatQuest: K-nearest neighbors, Clearly Explained
  37. 37 Standard Deviation vs Standard Error, Clearly Explained!!!
  38. 38 Stochastic Gradient Descent, Clearly Explained!!!
  39. 39 Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
  40. 40 Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
  41. 41 StatQuest: Hierarchical Clustering
  42. 42 Multiple Regression, Clearly Explained!!!
  43. 43 Quantiles and Percentiles, Clearly Explained!!!
  44. 44 StatQuest: PCA in R
  45. 45 Regression Trees, Clearly Explained!!!
  46. 46 RPKM, FPKM and TPM, Clearly Explained!!!
  47. 47 XGBoost Part 1 (of 4): Regression
  48. 48 Naive Bayes, Clearly Explained!!!
  49. 49 Logistic Regression Details Pt 3: R-squared and p-value
  50. 50 The Main Ideas behind Probability Distributions
  51. 51 Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!
  52. 52 ROC and AUC in R
  53. 53 The Binomial Distribution and Test, Clearly Explained!!!
  54. 54 Gradient Boost Part 2 (of 4): Regression Details
  55. 55 StatQuest: PCA in Python
  56. 56 StatQuest: MDS and PCoA
  57. 57 Pearson's Correlation, Clearly Explained!!!
  58. 58 Regularization Part 3: Elastic Net Regression
  59. 59 Gradient Boost Part 3 (of 4): Classification
  60. 60 StatQuest: A gentle introduction to ChIP-Seq
  61. 61 How to calculate p-values
  62. 62 The standard error, Clearly Explained!!!
  63. 63 What is a (mathematical) model?
  64. 64 Machine Learning Fundamentals: Sensitivity and Specificity
  65. 65 Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!
  66. 66 Machine Learning Fundamentals: Sensitivity and Specificity (old version)
  67. 67 Population and Estimated Parameters, Clearly Explained!!!
  68. 68 Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
  69. 69 Confidence Intervals, Clearly Explained!!!
  70. 70 StatQuest: Random Forests in R
  71. 71 StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
  72. 72 Sampling from a Distribution, Clearly Explained!!!
  73. 73 Multiple Regression in R, Step-by-Step!!!
  74. 74 Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
  75. 75 Ridge, Lasso and Elastic-Net Regression in R
  76. 76 Lowess and Loess, Clearly Explained!!!
  77. 77 StatQuest: PCA - Practical Tips
  78. 78 Linear Regression in R, Step-by-Step
  79. 79 Logs (logarithms), Clearly Explained!!!
  80. 80 Drawing and Interpreting Heatmaps
  81. 81 Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
  82. 82 StatQuest: DESeq2, part 1, Library Normalization
  83. 83 Sample Size and Effective Sample Size, Clearly Explained!!!
  84. 84 Saturated Models and Deviance
  85. 85 XGBoost Part 2 (of 4): Classification
  86. 86 Gaussian Naive Bayes, Clearly Explained!!!
  87. 87 How to Prune Regression Trees, Clearly Explained!!!
  88. 88 Gradient Boost Part 4 (of 4): Classification Details
  89. 89 Why Dividing By N Underestimates the Variance
  90. 90 StatQuest: Random Forests Part 2: Missing data and clustering
  91. 91 Fisher's Exact Test and the Hypergeometric Distribution
  92. 92 Deviance Residuals
  93. 93 Boxplots are Awesome!!!
  94. 94 Ridge vs Lasso Regression, Visualized!!!
  95. 95 Design Matrices For Linear Models, Clearly Explained!!!
  96. 96 Power Analysis, Clearly Explained!!!
  97. 97 The Difference Between Technical and Biological Replicates
  98. 98 Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!
  99. 99 Statistical Power, Clearly Explained!!!
  100. 100 StatQuest: The Trailer!
  101. 101 Quantile Normalization, Clearly Explained!!!
  102. 102 p-hacking and power calculations
  103. 103 StatQuest: One or Two Tailed P-Values
  104. 104 XGBoost Part 3 (of 4): Mathematical Details
  105. 105 StatQuest: edgeR and DESeq2, part 2 - Independent Filtering
  106. 106 Design Matrix Examples in R, Clearly Explained!!!
  107. 107 Neural Networks Pt. 1: Inside the Black Box
  108. 108 StatQuickie: Which t test to use
  109. 109 p-hacking: What it is and how to avoid it!
  110. 110 StatQuest: MDS and PCoA in R
  111. 111 Live 2020-03-16!!! Naive Bayes
  112. 112 XGBoost Part 4 (of 4): Crazy Cool Optimizations
  113. 113 Bam!!! Clearly Explained!!!
  114. 114 StatQuest: edgeR, part 1, Library Normalization
  115. 115 StatQuickie: Thresholds for Significance
  116. 116 Bar Charts Are Better than Pie Charts
  117. 117 Alternative Hypotheses: Main Ideas!!!
  118. 118 The Chain Rule
  119. 119 Live 2020-04-06!!! Naive Bayes: Gaussian
  120. 120 Live 2020-04-20!!! Expected Values
  121. 121 StatQuest: RNA-seq - the problem with technical replicates
  122. 122 Neural Networks Pt. 2: Backpropagation Main Ideas
  123. 123 Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
  124. 124 Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
  125. 125 Neural Networks Pt. 3: ReLU In Action!!!
  126. 126 Neural Networks Pt. 4: Multiple Inputs and Outputs
  127. 127 StatQuest: How to make a Mean Pizza Crust!!!
  128. 128 US Census Data and Contest!!!
  129. 129 Neural Networks Part 5: ArgMax and SoftMax
  130. 130 The SoftMax Derivative, Step-by-Step!!!
  131. 131 Neural Networks Part 6: Cross Entropy
  132. 132 Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
  133. 133 Neural Networks Part 8: Image Classification with Convolutional Neural Networks
  134. 134 Silly Songs, Clearly Explained!!!
  135. 135 Decision and Classification Trees, Clearly Explained!!!
  136. 136 How to make your own StatQuest!!!
  137. 137 Three (3) things to do when starting out in Data Science
  138. 138 Ken Jee's #66DaysOfData Challenge Clearly Explained!!!
  139. 139 Bootstrapping Main Ideas!!!
  140. 140 Using Bootstrapping to Calculate p-values!!!
  141. 141 Conditional Probabilities, Clearly Explained!!!
  142. 142 Conditional Probabilities, Clearly Explained!!!
  143. 143 Bayes' Theorem, Clearly Explained!!!!
  144. 144 Entropy (for data science) Clearly Explained!!!
  145. 145 Frank Starmer Clearly Explained (How my pop influenced StatQuest!!!)
  146. 146 p-values: What they are and how to interpret them
  147. 147 Clustering with DBSCAN, Clearly Explained!!!
  148. 148 Tensors for Neural Networks, Clearly Explained!!!
  149. 149 Troll 2, Clearly Explained!!!

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