Machine Learning
A subset of AI where systems learn from data to improve performance without being explicitly programmed.
Machine Learning (ML) is the practice of training algorithms on large datasets so they can identify patterns and make predictions or decisions — without a human writing explicit rules for every scenario. Instead of programming "if X then Y," you feed the system examples and it figures out the rules itself.
The three main paradigms are supervised learning (labeled data), unsupervised learning (unlabeled data), and reinforcement learning (reward-based feedback). Most AI products you use today — spam filters, recommendation engines, fraud detection — are built on supervised ML.
ML Learning Types
- Supervised — trained on labeled input/output pairs (e.g. image classification)
- Unsupervised — finds hidden structure in unlabeled data (e.g. clustering)
- Reinforcement — agent learns by taking actions and receiving rewards
- Self-supervised — generates its own labels from raw data (used in LLMs)
Deep learning is a powerful subset of ML that uses neural networks with many layers. It has driven most of the recent breakthroughs in language, vision, and audio AI. Understanding ML is foundational to understanding how any modern AI tool works.