Machine Learning
A branch of artificial intelligence where computers learn patterns from data to make predictions or decisions without being explicitly programmed.
Learning From Data
Traditional programming is like giving someone a recipe - you specify every step, every decision, every outcome. Machine learning flips this around. Instead of writing rules, you give the system examples, and it figures out the rules on its own.
Say you want to build a spam filter. The old way: manually define rules like "if email contains 'FREE MONEY' then spam." The machine learning way: show the system thousands of emails labeled spam or not spam, and let it learn what patterns distinguish them. It might discover things you'd never think to code explicitly.
Types of Machine Learning
Machine learning comes in several flavors. Supervised learning uses labeled examples - you tell the system the right answers during training. Unsupervised learning finds hidden patterns in unlabeled data. Reinforcement learning learns through trial and error, getting rewards for good actions.
What makes machine learning powerful is its flexibility. The same basic techniques can predict stock prices, recommend movies, detect fraud, translate languages, or diagnose diseases. You're essentially building systems that improve with experience. The more quality data they see, the better they get.