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