الانحدار والتصنيف

Estimated duration: 30 hours (6 modules, 18 lessons + project)

What you will learn

• Fit and interpret multiple linear regression and evaluate it with MAE, RMSE and R²
• Diagnose regression models with residuals, handle non-linearity, outliers and multicollinearity
• Control complexity with polynomial features and Ridge/Lasso regularisation chosen by cross-validation
• Fit and interpret logistic regression: probabilities, log-odds, odds ratios and log loss
• Build multi-class and non-linear classifiers (softmax, SVM, trees) and compare them
• Evaluate classifiers with confusion matrices, ROC/PR curves, AUC, thresholds and calibration

Who it is for: learners who want a deep, practical understanding of the two core supervised tasks

Prerequisites: CRS-GEN-ML-FUNDAMENTALS; algebra, basic probability