Machine Learning (ML) is a subset of artificial intelligence (AI) that involves the development of algorithms that enable computers to learn from and make decisions or predictions based on data. Unlike traditional programming, where specific instructions are coded, ML algorithms use statistical techniques to identify patterns and relationships within datasets, enabling the system to improve its performance over time without explicit programming. Applications of machine learning are widespread and include areas such as image and speech recognition, recommendation systems, autonomous vehicles, and predictive analytics.

Machine learning models are typically categorized into supervised learning, unsupervised learning, and reinforcement learning. In supervised learning, the model is trained on labeled data, where the correct output is known. Unsupervised learning deals with unlabeled data, focusing on discovering hidden patterns or intrinsic structures within the data. Reinforcement learning, on the other hand, involves an agent learning to make decisions by receiving feedback from its actions within an environment. As data continues to grow in volume and complexity, machine learning has become a crucial tool for making sense of big data and driving innovation across various industries.