Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Explores logistic regression fundamentals, including cost functions, regularization, and classification boundaries, with practical examples using scikit-learn.
Covers the basics of machine learning, supervised and unsupervised learning, various techniques like k-nearest neighbors and decision trees, and the challenges of overfitting.
