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
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
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