Supervised Machine Learning: Regression

This module introduces supervised machine learning fundamentals, focusing on regression applications. It covers linear regression concepts, best practices, error measurement, and model selection.

This module covers best practices to prevent overfitting, including training-test splits and polynomial features. It includes theoretical framework and hands-on examples.

This module explores cross-validation techniques to optimize the trade-off between training and testing data usage, enabling more effective model validation.

This module covers regularization techniques including Ridge, LASSO, and Elastic Net, exploring their pros and cons and implementation details.

This module provides in-depth understanding of the relationship between loss functions and different regularization types.

This module allows students to apply all learned concepts in a comprehensive project, testing their understanding of regression techniques and model optimization.