Tonton / Machine Learning Roadmap ML → 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 Word Embedding in PyTorch + Lightning Coding a ChatGPT Like Transformer From Scratch in PyTorch Reinforcement Learning: Essential Concepts Reinforcement Learning with Neural Networks: Essential Concepts Reinforcement Learning with Neural Networks: Mathematical Details Reinforcement Learning with Human Feedback (RLHF), Clearly Explained!!!
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