ML Fundamentals: Supervised Learning Algorithms Questions

Deep understanding of linear regression, logistic regression, decision trees, random forests, SVMs, and ensemble methods. Be able to explain: how each algorithm works, advantages/disadvantages, when to use each, regularization techniques (L1/L2), hyperparameter tuning, and how to handle overfitting.