Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. It involves algorithms that analyze data, identify patterns, and make decisions with minimal human intervention. Machine learning is broadly categorized into supervised learning, unsupervised learning, and reinforcement learning.

In supervised learning, algorithms are trained on labeled data, allowing them to predict outcomes based on input features. Unsupervised learning deals with unlabeled data, where algorithms identify hidden patterns or groupings. Reinforcement learning involves training models to make sequences of decisions by rewarding desired behaviors.

Machine learning is applied in various fields, including natural language processing, image recognition, and predictive analytics. It powers technologies like recommendation systems, autonomous vehicles, and fraud detection. By leveraging vast amounts of data, machine learning models can adapt and optimize their performance over time, leading to more accurate and efficient solutions.