Several types of tasks that aim at solving real-world problems can be achieved thanks to ML. An ML method generally means a group of specific types of algorithms that are suitable for solving a particular kind of problem and the method addresses any constraints that the problem brings along with it. For example, a constraint of a particular problem could be the availability of labeled data that can be provided as input to the learning algorithm.

Essentially, the popular ML methods are supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and transfer learning. The rest of this section details each of these methods.