Machine Learning Fundamentals

This module explores essential concepts and techniques in machine learning, including key topics such as linear methods (e.g., linear regression, lasso, ridge), tree methods (decision trees, random forests, boosting), and unsupervised methods like clustering. Learners will gain proficiency fitting various machine learning models, understanding regularization to prevent overfitting, and cross-validation to evaluate model performance and tune hyper-parameters.