Covers the basics of Machine Learning, including recognizing hand-written digits, supervised classification, decision boundaries, and polynomial curve fitting.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.
