What is machine learning and how does it work?

Machine learning (ML) is a subset of artificial intelligence that enables computers to learn patterns from data and make decisions or predictions without being explicitly programmed for each task. The core idea is to build a mathematical model that maps input data to desired outputs. The process typically involves three stages: data collection and preprocessing, model training, and inference. In training, a dataset of labeled examples is fed into an algorithm that adjusts internal parameters (weights) to minimize a loss function, often using gradient descent. Once trained, the model can generalize to unseen data, providing predictions or classifications. ML spans supervised, unsupervised, and reinforcement learning, each suited to different problem types. For example, a supervised image classifier learns from labeled pictures to recognize objects, while an unsupervised clustering algorithm groups similar data points without labels. The effectiveness of ML depends on data quality, feature representation, model complexity, and proper evaluation metrics.