Learning objectives
- • Derive the intuition behind MSE, log loss, L1, and L2 regularization
- • Explain coefficient interpretation, multicollinearity, and feature scaling
- • Connect thresholding and calibration to business decisions
Traditional ML
Linear Models and Regularization
Prepare regression, logistic regression, loss functions, regularization, optimization, and coefficient interpretation.
Recommended on day 1090 minutesBeginner
Interview prompts
- • Why does L1 regularization produce sparse weights?
- • How do you interpret logistic regression coefficients?
Prerequisites