Machine Learning

topic Linear Regression Modeling relationships with least squares, gradient descent, and regularization. read topic Logistic Regression Binary classification, sigmoid function, cross-entropy loss, and decision boundaries. read topic K-Means Clustering Unsupervised clustering, centroid initialization (k-means++), elbow method, and silhouette score. read topic Decision Trees Recursive partitioning, information gain, Gini impurity, pruning, and interpretability. read topic Random Forests Ensemble learning, bagging, feature randomness, out-of-bag error, and variable importance. read topic Neural Networks Perceptrons, activation functions, backpropagation, and universal approximation theorem. read topic Transformers Self-attention, multi-head attention, positional encoding, and the Transformer architecture. read topic Convolutional Networks Convolution, pooling, feature maps, and architectures (ResNet, VGG, Inception). read topic Recurrent Networks Sequential data, LSTMs, GRUs, vanishing gradients, and sequence-to-sequence models. read topic Gradient Descent Stochastic, mini-batch, momentum, Adam, learning rate schedules, and convergence. read topic Overfitting Bias-variance tradeoff, regularization (L1/L2), dropout, early stopping, and data augmentation. read topic Evaluation Metrics Accuracy, precision, recall, F1, ROC-AUC, confusion matrix, and cross-validation. read topic Feature Engineering Scaling, encoding, feature selection, PCA, and domain-specific feature creation. read topic Reinforcement Learning Markov decision processes, Q-learning, policy gradients, deep RL, and exploration vs exploitation. read