Types of Machine Learning

Machine learning algorithms learn from data to make predictions or decisions. These algorithms are broadly categorized into supervised, unsupervised, and reinforcement learning. Supervised learning uses labeled data to train a model to map inputs to outputs. Unsupervised learning, on the other hand, works with unlabeled data to discover hidden patterns and structures. Reinforcement learning involves training an agent to make decisions in an environment to maximize a reward. More recently, semi-supervised learning, which uses a combination of labeled and unlabeled data, and self-supervised learning, where the data itself provides the supervision signal, have gained prominence.