Advancements in artificial intelligence (AI) have reshaped modern society, and the continuous development of apps, tools, and platforms is nowadays incontrollable. In this context, machine learning (ML) is a subcategory of AI that involves creating algorithms and statistical models that facilitate computers to learn from data and make decisions or predictions. In fact, ML can learn from experience without being explicitly programmed for every single task. After presenting the key components of ML, the four main groups of ML are defined, namely, supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning methodologies. For these processes, ML uses algorithms to discern patterns and make predictions or decisions based on data, eliminating the necessity for explicit human programming. In this context, some of the most usual ML algorithms that can be applied to most data problems are neural networks, clustering, linear regression, logistic regression, decision trees, and random forests. Finally, the connections between AI and language are presented, focusing then on the applications of ML in language education.