Linear regression and logistic regression, Decision trees, Random Forest, Gradient Boosting (XGBoost, LightGBM, CatBoost), Support Vector Machines (SVMs) and kernel methods, Neural networks — CNNs, RNNs, LSTMs, and Transformers, Classification, regression, and ranking problems, Cross-validation, bias-variance trade-off, regularization (L1/L2, dropout). Design and implement supervised, unsupervised, and reinforcement learning models tailored to complex business problems.