Internship← Artificial Intelligence & Machine Learning learning pathArtificial Intelligence & Machine Learning · Module 4 of 10
Regression models
Predict numbers with regression.
📖 Key ideas
- Linear regression fits a line: y = mx + c.
- Multiple regression uses many features.
- Metrics: MAE, MSE, RMSE, R².
- Polynomial regression captures curves.
- Regularisation (Ridge, Lasso) reduces overfitting.
💡 Real-life example: Regression draws the best straight road through scattered houses to predict where the next one lies.
Linear regressionFits a straight-line relationship
RMSERoot mean squared error
R²Variance explained
LassoL1-regularised regression