Machine Learning

Machine-learning models are trained using examples rather than having every rule written manually. They are used for forecasting, recommendations, anomaly detection, vision, language processing, risk scoring, and many other tasks where patterns can be learned from historical data. Understanding the concept helps practitioners choose training methods, evaluate model quality, and reduce errors when predictions are used in production systems.