Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.

Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.

Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.

Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.

Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.