Machine Learning represents a fundamental shift in how computers are programmed, moving away from explicit instructions to a data-driven approach where algorithms learn patterns and make decisions based on input data. This field encompasses a variety of methods, including supervised learning (where models are trained on labeled data), unsupervised learning (where models identify patterns in data without labels), and reinforcement learning (where models learn to make sequences of decisions). ML is crucial for applications ranging from natural language processing and computer vision to predicting consumer behavior and developing autonomous systems.

The concept of Machine Learning was formally introduced in 1959 by Arthur Samuel, who developed a checkers-playing program that could improve its performance with experience. However, the term and its underlying concepts gained significant popularity in the late 1990s and early 2000s as computational power increased, enabling the practical application of complex models to large data sets.

Alongside Arthur Samuel, other notable figures in the development of Machine Learning include Tom Mitchell, who provided a widely accepted formal definition of the algorithms involved in ML; and Geoffrey Hinton, who made significant contributions to neural networks and deep learning, a subset of ML that has been responsible for many of the field's recent advancements.