Regularization Part 1 - Ridge (L2) Regression

This course explains ridge regression as an L2 regularization technique for reducing overfitting and variance. It covers the penalty parameter, applications to continuous and discrete variables, logistic regression, and situations with limited data, assuming familiarity with bias-variance, linear models, and cross-validation.

Syllabus

Awesome song and introduction
Ridge Regression main ideas
Ridge Regression details
Ridge Regression for discrete variables
Ridge Regression for Logistic Regression
Ridge Regression for fancy models
Ridge Regression when you don't have much data
Summary of concepts
I meant to say "Negative Log-Likelihood" instead of "Likelihood".