Grow your professional career with our advanced machine learning courses.
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.
Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
Forecast single and multiple time series with machine learning models. Implement backtesting to evaluate models before deployment.
Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.
Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.
Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.