Feature Engineering
Subject Overview
The first course in the AI/ML elective stream, covering the importance of features in machine learning, data types, basic preprocessing (missing data, scaling, normalization), and feature engineering techniques for numerical and categorical data including binning, encoding, feature selection methods and PCA. A 2-credit elective theory paper.
Unit-wise Syllabus
2 units — click WhatsApp below to get the full notes for each
Unit 1: Introduction to feature engineering
Importance of features in machine learning, data and feature types (numerical, categorical, ordinal, discrete, continuous, interval, ratio), basic preprocessing — handling missing data, data cleaning, feature scaling, normalization and transformation
Unit 2: Feature engineering techniques
Binning and discretization, polynomial and interaction features, one-hot and label encoding, feature extraction vs feature selection, filter/wrapper/hybrid selection methods, feature reduction via Principal Component Analysis