Introduces key machine learning concepts, such as supervised learning, regression vs. classification, and the K-Nearest Neighbors algorithm.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
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.
Introduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Covers the basics of machine learning, challenges in deployment, adversarial attacks, and privacy concerns.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
