Supervised Learning - This type involves training a model on labeled data, where the input-output pairs are known. It's commonly used for tasks like classification and regression.
Unsupervised Learning - In this type, the model is trained on unlabeled data and must find patterns and relationships within the data. Clustering and association are typical applications.
Reinforcement Learning - This type focuses on training models to make sequences of decisions by rewarding desired behaviors and punishing undesired ones. It's often used in robotics and game AI.
Semi-Supervised Learning - This type combines a small amount of labeled data with a large amount of unlabeled data during training. It is useful when labeling data is expensive or time-consuming.
Self-Supervised Learning - A newer approach where the model generates its own labels from the input data, often used in natural language processing and computer vision.