The different types of machine learning include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves training a model on labeled datasets, allowing it to learn from input-output mappings. Unsupervised learning, on the other hand, deals with unlabeled data, identifying patterns and structures without specific guidance. Reinforcement learning focuses on training models through trial and error, receiving rewards or penalties based on their actions. Each type serves different purposes and is employed across various fields such as computer vision, natural language processing, and robotics.