Machine learning is the field that has brought AI to its success in the last years. Large language models and generative models, which are based on machine learning, have a large number of users. In this chapter we explain how machine learning models are built. We begin with simple models and then move into neural networks and deep learning that are currently the most successful ones. To illustrate that these are not the only existing models we also explain decision trees. They are a completely different type of model that are also effective in some applications. Then, we explain language models in terms of neural networks. The chapter finishes reviewing a few concepts connected with machine learning. We include concepts as outliers, missing data, bias, explainability, correlation, and causation.