Machine learning is a subset of artificial intelligence focused on building systems that learn patterns from data without explicit programming. At its core, machine learning involves training mathematical models on historical data to make predictions or decisions on new, unseen data. Understanding the bias-variance tradeoff is fundamental: models must balance the ability to capture complex patterns (low bias) against stability across different datasets (low variance), as optimizing one often degrades the other.