Feature Engineering for Machine Learning
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
- Course
- By Soledad Galli
Feature Selection for Machine Learning
Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
- Course
- By Soledad Galli
Hyperparameter Optimization for Machine Learning
Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.
- Course
- By Soledad Galli
Machine Learning with Imbalanced Data
Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.
- Course
- By Soledad Galli
Feature Engineering for Time Series Forecasting
Create lag, window and seasonal features, perform imputation and encoding, extract datetime variables, remove outliers, and more.
- Course
- By Kishan Manani
Machine Learning Interpretability
Explain interpretable and black box models with LIME, Shap, partial dependency plots and more.
- Course
- By Soledad Galli
Forecasting with Machine Learning
Forecast single and multiple time series with machine learning models. Implement backtesting to evaluate models before deployment.
- Course
- By Kishan Manani
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