This module provides a technical foundation in supervised machine learning covering data-label mapping, regression modeling (Linear and Polynomial), regularization techniques (L1/L2) for overfitting control, and classification using Logistic Regression. It also includes detailed coverage of model performance evaluation using confusion matrix metrics, bias-variance tradeoff concepts, and K-Fold Cross Validation strategies used in production ML pipelines.The module prepares learners to understand end-to-end supervised ML workflows including data preprocessing considerations, model training lifecycle, validation strategy selection, and performance interpretation for enterprise deployment scenarios.
This module covers the implementation and optimization of the k-Nearest Neighbors (k-NN) algorithm, including distance metrics (Euclidean, Manhattan), feature scaling impact, hyperparameter tuning (K selection), and computational trade-offs in real-time prediction environments.The module concludes with an end-to-end supervised learning mini project covering dataset preparation, feature engineering, model training, validation, performance benchmarking, and result interpretation aligned with production ML workflow standards.
