Machine learning is a subset of artificial intelligence that focuses on developing algorithms and models that enable computers to learn and make predictions or decisions based on data. Instead of being explicitly programmed, these algorithms learn patterns from data and improve their performance over time.

Machine learning can be divided into three main types:

1. Supervised Learning: In this type, the algorithm is trained on a labeled dataset, where the input data is paired with the correct output. The goal is to learn a mapping from inputs to outputs so that the algorithm can make accurate predictions on new, unseen data.
2. Unsupervised Learning: Unsupervised learning deals with unlabeled data. The goal here is to find patterns, relationships, or structures within the data. Clustering and dimensionality reduction are common tasks in this category.
3. Reinforcement Learning: This type involves training agents to interact with an environment and learn how to make decisions to maximize a reward. It’s commonly used in tasks like training AI agents to play games.