Machine learning splits into three main paradigms based on how models learn from data.

Supervised Learning

Learns from labeled examples (input → known output). Used for: classification, regression. Examples: spam detection, house price prediction.

Unsupervised Learning

Finds patterns in unlabeled data. Used for: clustering, dimensionality reduction. Examples: customer segmentation, anomaly detection.

Reinforcement Learning

Agent learns by taking actions and receiving rewards or penalties. Used for: game AI, robotics, recommendation systems. Example: AlphaGo, ChatGPT's RLHF training.

Reference:

Google ML Crash Course

https://developers.google.com/machine-learning/crash-course