Supervised and unsupervised algorithms for building, tuning and evaluating predictive models.
Machine learning courses in this section focus on classical algorithms: linear and logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-means and other clustering methods. You will learn the full modelling workflow, including feature engineering, train-test splits, cross-validation, handling class imbalance and choosing evaluation metrics that reflect the actual business cost of an error. Most courses build on scikit-learn and XGBoost in Python, with some R alternatives. Neural network and transformer material lives under Deep Learning; this is the ground you should cover first, because most production problems are still solved with these methods.
