An exhaustive introduction to machine learning fundamentals. Covers the ML landscape, gradient descent, bias-variance tradeoff, linear regression, logistic regression, decision trees, ensembles, SVMs, neural networks, clustering, dimensionality reduction, and model evaluation metrics.

An exhaustive algorithm reference spanning classical ML, gradient boosting, time series, deep learning, and AutoML. Part 1 covers sklearn foundations (Ridge, Lasso, Trees, SVMs, KNN). Part 2 covers advanced algorithms (XGBoost, LightGBM, CatBoost, ARIMA, Prophet, Anomaly Detection). Part 3 covers deep learning (PyTorch, Keras, CNNs, RNNs, Transformers, RL, AutoML).