Machine Learning

Machine learning is a technology in which computers automatically learn patterns and relationships from data—rather than having all rules explicitly programmed by humans—to perform tasks such as prediction and classification. It is one of the core fields of artificial intelligence (AI) and underpins most modern AI services. The defining characteristic of machine learning is "the ability to learn from experience (data)." It builds models from large volumes of data that can then make inferences and judgments about new data. For example, systems that identify cats vs. dogs in images, or that suggest product recommendations based on past purchase history, are all applications of machine learning. There are three main learning paradigms: • Supervised learning: Build a predictive model from labeled data (e.g., spam classification, sales forecasting) • Unsupervised learning: Find structure in unlabeled data (e.g., customer clustering) • Reinforcement learning: Learn through trial and error to maximize a reward (e.g., game AI, autonomous driving) In recent years, deep learning has emerged as an advanced approach, delivering breakthrough results in areas such as speech recognition, image recognition, and natural language processing. Machine learning is applied across industries—business, medicine, finance, manufacturing, and education—and interest in questions such as "what is machine learning," "how does it work," and "what types are there" continues to grow.