Explores model selection, evaluation, and generalization in machine learning, emphasizing unbiased performance estimation and the risks of over-learning.
Covers overfitting, regularization, and cross-validation in machine learning, exploring polynomial curve fitting, feature expansion, kernel functions, and model selection.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.