StatQuest

Watch and track your favorite playlist.

Curated by: StatQuest with Josh Starmer (178 videos)

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Tracks in this Playlist

• StatQuest: Principal Component Analysis (PCA), Step-by-Step
• StatQuest: Logistic Regression
• In Statistics, Probability is not Likelihood.
• Maximum Likelihood, clearly explained!!!
• Private video
• StatQuest: PCA main ideas in only 5 minutes!!!
• StatQuest: Decision Trees
• Machine Learning Fundamentals: Bias and Variance
• StatQuest: K-means clustering
• StatQuest: Random Forests Part 1 - Building, Using and Evaluating
• ROC and AUC, Clearly Explained!
• Regularization Part 1: Ridge (L2) Regression
• Support Vector Machines Part 1 (of 3): Main Ideas!!!
• Gradient Descent, Step-by-Step
• Logistic Regression Details Pt1: Coefficients
• StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
• Machine Learning Fundamentals: Cross Validation
• Linear Regression, Clearly Explained!!!
• A Gentle Introduction to Machine Learning
• AdaBoost, Clearly Explained
• StatQuest: A gentle introduction to RNA-seq
• Gradient Boost Part 1 (of 4): Regression Main Ideas
• Maximum Likelihood For the Normal Distribution, step-by-step!!!
• Logistic Regression in R, Clearly Explained!!!!
• Quantile-Quantile Plots (QQ plots), Clearly Explained!!!
• p-values: What they are and how to interpret them
• StatQuest: t-SNE, Clearly Explained
• Regularization Part 2: Lasso (L1) Regression
• Machine Learning Fundamentals: The Confusion Matrix
• R-squared, Clearly Explained!!!
• The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
• Covariance, Clearly Explained!!!
• Logistic Regression Details Pt 2: Maximum Likelihood
• The Normal Distribution, Clearly Explained!!!
• StatQuest: Histograms, Clearly Explained
• StatQuest: K-nearest neighbors, Clearly Explained
• Standard Deviation vs Standard Error, Clearly Explained!!!
• Stochastic Gradient Descent, Clearly Explained!!!
• Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
• Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
• StatQuest: Hierarchical Clustering
• Multiple Regression, Clearly Explained!!!
• Quantiles and Percentiles, Clearly Explained!!!
• StatQuest: PCA in R
• Regression Trees, Clearly Explained!!!
• RPKM, FPKM and TPM, Clearly Explained!!!
• XGBoost Part 1 (of 4): Regression
• Naive Bayes, Clearly Explained!!!
• Logistic Regression Details Pt 3: R-squared and p-value
• The Main Ideas behind Probability Distributions
• Calculating the Mean, Variance and Standard Deviation, Clearly Explained!!!
• ROC and AUC in R
• The Binomial Distribution and Test, Clearly Explained!!!
• Gradient Boost Part 2 (of 4): Regression Details
• StatQuest: PCA in Python
• StatQuest: MDS and PCoA
• Pearson's Correlation, Clearly Explained!!!
• Regularization Part 3: Elastic Net Regression
• Gradient Boost Part 3 (of 4): Classification
• StatQuest: A gentle introduction to ChIP-Seq
• How to calculate p-values
• The standard error, Clearly Explained!!!
• What is a (mathematical) model?
• Machine Learning Fundamentals: Sensitivity and Specificity
• Maximum Likelihood for the Exponential Distribution, Clearly Explained!!!
• Machine Learning Fundamentals: Sensitivity and Specificity (old version)
• Population and Estimated Parameters, Clearly Explained!!!
• Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
• Confidence Intervals, Clearly Explained!!!
• StatQuest: Random Forests in R
• StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
• Sampling from a Distribution, Clearly Explained!!!
• Multiple Regression in R, Step-by-Step!!!
• Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
• Ridge, Lasso and Elastic-Net Regression in R
• Lowess and Loess, Clearly Explained!!!
• StatQuest: PCA - Practical Tips
• Linear Regression in R, Step-by-Step
• Logs (logarithms), Clearly Explained!!!
• Drawing and Interpreting Heatmaps
• Maximum Likelihood for the Binomial Distribution, Clearly Explained!!!
• StatQuest: DESeq2, part 1, Library Normalization
• Sample Size and Effective Sample Size, Clearly Explained!!!
• Saturated Models and Deviance
• XGBoost Part 2 (of 4): Classification
• Gaussian Naive Bayes, Clearly Explained!!!
• How to Prune Regression Trees, Clearly Explained!!!
• Gradient Boost Part 4 (of 4): Classification Details
• Why Dividing By N Underestimates the Variance
• StatQuest: Random Forests Part 2: Missing data and clustering
• Fisher's Exact Test and the Hypergeometric Distribution
• Deviance Residuals
• Boxplots are Awesome!!!
• Ridge vs Lasso Regression, Visualized!!!
• Design Matrices For Linear Models, Clearly Explained!!!
• Power Analysis, Clearly Explained!!!
• The Difference Between Technical and Biological Replicates
• Hypothesis Testing and The Null Hypothesis, Clearly Explained!!!
• Statistical Power, Clearly Explained!!!
• StatQuest: The Trailer!