Machine Learning

Machine Learning covers a lot of topics and this can be intimidating. However, there is no reason to fear, this play list will help you trough it all, one step at a time.

Curated by: StatQuest with Josh Starmer (106 videos)

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Tracks in this Playlist

• A Gentle Introduction to Machine Learning
• Machine Learning Fundamentals: Cross Validation
• Machine Learning Fundamentals: The Confusion Matrix
• Machine Learning Fundamentals: Sensitivity and Specificity
• The Sensitivity, Specificity, Precision, Recall Sing-a-Long!!!
• Machine Learning Fundamentals: Bias and Variance
• Entropy (for data science) Clearly Explained!!!
• Mutual Information, Clearly Explained!!!
• The Main Ideas of Fitting a Line to Data (The Main Ideas of Least Squares and Linear Regression.)
• Linear Regression, Clearly Explained!!!
• Multiple Regression, Clearly Explained!!!
• Using Linear Models for t-tests and ANOVA, Clearly Explained!!!
• Design Matrices For Linear Models, Clearly Explained!!!
• Odds and Log(Odds), Clearly Explained!!!
• Odds Ratios and Log(Odds Ratios), Clearly Explained!!!
• StatQuest: Logistic Regression
• Logistic Regression Details Pt1: Coefficients
• Logistic Regression Details Pt 2: Maximum Likelihood
• Logistic Regression Details Pt 3: R-squared and p-value
• Saturated Models and Deviance
• Logistic Regression in R, Clearly Explained!!!!
• Deviance Residuals
• ROC and AUC, Clearly Explained!
• ROC and AUC in R
• Regularization Part 1: Ridge (L2) Regression
• Regularization Part 2: Lasso (L1) Regression
• Ridge vs Lasso Regression, Visualized!!!
• Regularization Part 3: Elastic Net Regression
• Ridge, Lasso and Elastic-Net Regression in R
• StatQuest: Principal Component Analysis (PCA), Step-by-Step
• StatQuest: PCA main ideas in only 5 minutes!!!
• StatQuest: PCA - Practical Tips
• StatQuest: PCA in R
• StatQuest: PCA in Python
• StatQuest: Linear Discriminant Analysis (LDA) clearly explained.
• Bam!!! Clearly Explained!!!
• StatQuest: MDS and PCoA
• StatQuest: MDS and PCoA in R
• StatQuest: t-SNE, Clearly Explained
• StatQuest: Hierarchical Clustering
• StatQuest: K-means clustering
• Clustering with DBSCAN, Clearly Explained!!!
• StatQuest: K-nearest neighbors, Clearly Explained
• Naive Bayes, Clearly Explained!!!
• Gaussian Naive Bayes, Clearly Explained!!!
• Decision and Classification Trees, Clearly Explained!!!
• StatQuest: Decision Trees, Part 2 - Feature Selection and Missing Data
• Regression Trees, Clearly Explained!!!
• How to Prune Regression Trees, Clearly Explained!!!
• One-Hot, Label, Target and K-Fold Target Encoding, Clearly Explained!!!
• Classification Trees in Python from Start to Finish
• StatQuest: Random Forests Part 1 - Building, Using and Evaluating
• StatQuest: Random Forests Part 2: Missing data and clustering
• StatQuest: Random Forests in R
• The Chain Rule, Clearly Explained!!!
• Gradient Descent, Step-by-Step
• Stochastic Gradient Descent, Clearly Explained!!!
• AdaBoost, Clearly Explained
• Gradient Boost Part 1 (of 4): Regression Main Ideas
• Gradient Boost Part 2 (of 4): Regression Details
• Gradient Boost Part 3 (of 4): Classification
• Gradient Boost Part 4 (of 4): Classification Details
• Troll 2, Clearly Explained!!!
• XGBoost Part 1 (of 4): Regression
• XGBoost Part 2 (of 4): Classification
• XGBoost Part 3 (of 4): Mathematical Details
• XGBoost Part 4 (of 4): Crazy Cool Optimizations
• XGBoost in Python from Start to Finish
• CatBoost Part 1: Ordered Target Encoding
• CatBoost Part 2: Building and Using Trees
• Cosine Similarity, Clearly Explained!!!
• Support Vector Machines Part 1 (of 3): Main Ideas!!!
• Support Vector Machines Part 2: The Polynomial Kernel (Part 2 of 3)
• Support Vector Machines Part 3: The Radial (RBF) Kernel (Part 3 of 3)
• Support Vector Machines in Python from Start to Finish.
• The Essential Main Ideas of Neural Networks
• Neural Networks Pt. 2: Backpropagation Main Ideas
• Backpropagation Details Pt. 1: Optimizing 3 parameters simultaneously.
• Backpropagation Details Pt. 2: Going bonkers with The Chain Rule
• Neural Networks Pt. 3: ReLU In Action!!!
• Neural Networks Pt. 4: Multiple Inputs and Outputs
• Neural Networks Part 5: ArgMax and SoftMax
• The SoftMax Derivative, Step-by-Step!!!
• Neural Networks Part 6: Cross Entropy
• Neural Networks Part 7: Cross Entropy Derivatives and Backpropagation
• Neural Networks Part 8: Image Classification with Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs), Clearly Explained!!!
• Long Short-Term Memory (LSTM), Clearly Explained
• Word Embedding and Word2Vec, Clearly Explained!!!
• Sequence-to-Sequence (seq2seq) Encoder-Decoder Neural Networks, Clearly Explained!!!
• Attention for Neural Networks, Clearly Explained!!!
• Transformer Neural Networks, ChatGPT's foundation, Clearly Explained!!!
• Decoder-Only Transformers, ChatGPTs specific Transformer, Clearly Explained!!!
• Encoder-Only Transformers (like BERT) for RAG, Clearly Explained!!!
• Tensors for Neural Networks, Clearly Explained!!!
• Essential Matrix Algebra for Neural Networks, Clearly Explained!!!
• The matrix math behind transformer neural networks, one step at a time!!!
• The StatQuest Introduction to PyTorch
• Introduction to Coding Neural Networks with PyTorch and Lightning
• Long Short-Term Memory with PyTorch + Lightning