Feature engineering for forecasting
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Feature Engineering for Machine Learning
Learn imputation, variable encoding, discretization, feature extraction, how to work with datetime, outliers, and more.
Feature Selection for Machine Learning
Learn filter, wrapper, and embedded methods, recursive feature elimination, exhaustive search, feature shuffling & more.
Hyperparameter Optimization for Machine Learning
Learn grid and random search, Bayesian optimization, multi-fidelity models, Optuna, Hyperopt, Scikit-Optimize & more.
Machine Learning with Imbalanced Data
Learn to over-sample and under-sample your data, apply SMOTE, ensemble methods, and cost-sensitive learning.
Our Python Open-Source
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