Introduces nonlinear ML algorithms, covering nearest neighbor, k-NN, polynomial curve fitting, model complexity, overfitting, and regularization.
Covers the basics of machine learning, including supervised and unsupervised techniques, linear regression, and model training.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers the basics of linear regression in machine learning, including model training, loss functions, and evaluation metrics.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
