Supervised Learning: Regression — Machine Learning Roadmap
Predicting continuous numeric outcomes from data
Steps in Supervised Learning: Regression
- Linear Regression — intermediate · Fitting a straight-line relationship between features and a continuous target
- Polynomial Regression — intermediate · Fitting curved relationships by adding polynomial feature terms
- Regularization (Ridge, Lasso, Elastic Net) — intermediate · Penalizing large coefficients to reduce overfitting
- Regression Evaluation Metrics (MAE, MSE, RMSE, R²) — intermediate · Choosing the right metric to judge how good a regression model actually is
- Gradient Descent for Regression — intermediate · How regression coefficients are actually learned iteratively from data
Part of
- Machine Learning roadmap — the full learning path