What you'll learn

High-dimensional data with correlated and irrelevant predictors inflates coefficient variance and produces models that memorize the training data. In this course, Regularized Regression Techniques, you'll gain the ability to diagnose that problem and correct it with the right penalty. First, you'll explore multicollinearity and overfitting, how correlated or irrelevant predictors destabilize coefficient estimates, the general concept of a penalty term added to a loss function, the tradeoff of increased bias for reduced variance, where a penalized fit outperforms an unpenalized one, and why feature standardization matters when penalties depend on coefficient size. Next, you'll discover ridge regression and its squared penalty, watching coefficients shrink toward zero on a coefficient path plot as penalty strength rises. Then you'll learn how the lasso penalty differs mathematically, drives coefficients exactly to zero for implicit feature selection, struggles with correlated groups, and how elastic net resolves that. Finally, you'll learn how cross-validation selects an optimal regularization strength and how proper procedure prevents data leakage. When you're finished, you'll have the regularized regression skills needed to produce models that generalize.