There are three main types of machine learning:

1. Supervised Learning: In this type of learning, the algorithm is trained on labeled data, which means the input data is already tagged with the correct output. The goal of supervised learning is to learn a mapping between input and output variables, so that given new inputs, the algorithm can predict the correct output.

2. Unsupervised Learning: In unsupervised learning, the algorithm is not given any labeled data. Instead, it must find patterns and relationships in the input data on its own. The goal of unsupervised learning is to discover hidden structures in the data and group similar inputs together.

3. Reinforcement Learning: In reinforcement learning, the algorithm learns by interacting with an environment. The algorithm receives rewards or penalties based on its actions, and its goal is to learn a policy that maximizes its rewards over time. Reinforcement learning is often used in robotics and game playing applications.

There are also other types of machine learning, such as semi-supervised learning, which combines labeled and unlabeled data, and transfer learning, which allows a model trained on one task to be used for another related task.