Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Explores underfitting, overfitting, hyperparameters, bias-variance trade-off, and model evaluation in machine learning.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Explores decision trees for classification, entropy, information gain, one-hot encoding, hyperparameter optimization, and random forests.
