Machine Learning (ML) is a subset of Artificial Intelligence (AI) focused on creating algorithms that learn patterns from data and make predictions or decisions without explicit programming for each task. Instead of hard-coded rules, ML models improve their performance through experience—optimizing parameters based on training data to generalize well to unseen data.

Core Principle: ML works by representing data numerically (features), feeding it into algorithms, and adjusting model parameters to minimize prediction errors. The ultimate goal is generalization—performing accurately on new, real-world data.

Main Learning Paradigms:

• Supervised Learning: Trains on labeled datasets with known outputs. Used for classification (e.g., spam detection) and regression (e.g., price prediction). Optimization involves minimizing a loss function against ground truth.

• Unsupervised Learning: Works with unlabeled data to find hidden patterns, clusters, or correlations. Examples include K-means clustering and PCA for dimensionality reduction.

• Reinforcement Learning (RL): An agent learns by interacting with an environment, receiving rewards or penalties to maximize long-term gains. Common in robotics, gaming, and autonomous systems.