Machine learning is a branch of artificial intelligence that involves the development of algorithms and statistical models that allow computer systems to learn from and make predictions or decisions based on input data. In other words, machine learning is a way of teaching computers to learn from experience, just as humans do.

The core idea behind machine learning is to enable computers to automatically improve their performance on a task by learning from experience. This is typically done by training machine learning models on large datasets of labeled or unlabeled data, using techniques such as supervised, unsupervised, or reinforcement learning.

Supervised learning involves providing a machine learning model with labeled training data, in which each input data point is associated with a corresponding output label or target value. The model learns to generalize from the training data to make accurate predictions on new, unseen data.

Unsupervised learning involves training a machine learning model on unlabeled data, where the model must find patterns or structure in the data without the aid of labeled targets.

Reinforcement learning involves training a machine learning model to make decisions or take actions in an environment, with the goal of maximizing a reward signal.

Machine learning has become increasingly important in many areas, including computer vision, natural language processing, speech recognition, robotics, and many others. It has enabled breakthroughs in fields such as self-driving cars, facial recognition, and voice assistants, and is expected to continue to drive innovation and change in many industries.