The document outlines 5 main types of machine learning: supervised learning, unsupervised learning, reinforcement learning, semi-supervised learning, and self-supervised learning. Supervised learning uses labeled training data to predict future events within known classifications. Unsupervised learning finds patterns in unlabeled data through techniques like clustering. Reinforcement learning provides feedback to improve model performance. Semi-supervised learning uses both labeled and unlabeled data, generating pseudo-labels for unlabeled data. Self-supervised learning trains models to learn parts of the input from other parts without explicit labels.