Linear Algebra, Calculus, and Probability essentials.
Introduction to ML concepts, history, and types of learning.
Regression, Classification, and key algorithms.
Clustering, Dimensionality Reduction, and Association.
Agents, Environments, Rewards, and Q-Learning.
Image processing, CNNs, Object Detection, and Segmentation.