Machine learning (ML) is a subfield of artificial intelligence that focuses on the development of algorithms and models that allow computers to learn and improve from experience, without being explicitly programmed.

ML algorithms are designed to identify patterns and relationships within data, and to use these insights to make predictions, decisions, or identify anomalies in new data. The algorithms are trained on a large set of data, called a training dataset, and use statistical methods to identify patterns and correlations in the data.

The learning can be supervised, unsupervised, or semi-supervised, depending on the type of data and the specific learning task. In supervised learning, the algorithm is trained on labeled data, where the correct answers are already known. In unsupervised learning, the algorithm learns from unlabeled data, where there are no correct answers to learn from. In semi-supervised learning, the algorithm learns from a combination of labeled and unlabeled data.

Machine learning has a wide range of applications, including image recognition, natural language processing, fraud detection, recommendation systems, and predictive analytics.