Supervised Learning

Status: 🚧 Coming soon — a dedicated track is being written.

Supervised learning is the most common form of machine learning: you give the model labeled examples (input → target) and it learns the mapping. Regression predicts numbers; classification predicts categories.

What this section will cover

• Regression — Linear, Polynomial, Ridge, Lasso, ElasticNet
• Classification — Logistic regression, k-NN, Naive Bayes, SVM
• Tree-based models — Decision trees, Random Forests, Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Loss functions, optimization, regularization
• Model selection: cross-validation, hyperparameter search
• Imbalanced data, calibration, threshold tuning

Currently available — related material

The Machine Learning fundamentals section already covers most supervised techniques:

• Linear Regression (deep dive)
• Linear regression tutorial — W3Schools-style
• Polynomial & Regularization
• Logistic Regression & Classification
• Ensembles

A consolidated, end-to-end supervised-learning track lands here next.