What are the different types of Machine Learning? Explain Supervised, Unsupervised, and Reinforcement Learning with examples.
The three main types of Machine Learning are categorized by the kind of supervision (labels) the model receives.
Supervised Learning — model learns from labeled data (input → known output). Example: predicting house prices (regression), classifying emails as spam (classification).
Unsupervised Learning — model learns from unlabeled data, finding structure on its own. Example: customer segmentation (K-Means), dimensionality reduction (PCA).
Reinforcement Learning — an agent learns by interacting with an environment, receiving rewards for good actions. Example: game-playing AIs (AlphaGo), robotic control, RLHF for fine-tuning LLMs.
Modern variants: Semi-Supervised (some labels) and Self-Supervised (labels derived from data itself) are the foundation of modern LLMs.
Don't just list types — give one crisp example per type and the type of label (or lack of it). Bonus points for mentioning Semi-Supervised and Self-Supervised as the modern variants powering LLM pretraining.