Feature Engineering and Selection

This course introduces the essential techniques used to prepare, transform, create, and select features for machine learning models. Students learn data pre-processing, missing-value handling, categorical encoding, feature scaling, feature extraction, statistical feature selection, wrapper methods, embedded methods, and dimensionality reduction. Using Python, Pandas, NumPy, and Scikit-learn, learners apply these techniques to real-world datasets, improve model performance, reduce dimensionality, and avoid data leakage.