How Does Machine Learning Work?
Machine learning is how a system improves from examples instead of following a fixed rulebook. A machine learns by making predictions, measuring error, and adjusting — again and again — so it gets better on the patterns in its data.
How does a machine learn from data?
The model sees examples, produces an output, and compares that output with a target or signal. The difference — prediction error — guides small updates. Over many passes, those updates are how AI learns from data.
Prediction, error, and adjustment
Nothing mystical happens in one step. Learning is the pile of tiny corrections: predict, measure the miss, nudge parameters, repeat. That is machine learning explained at the level that matters for intuition.
Training versus new cases
Doing well on the training examples is not enough. The real test is new inputs the system has not memorized — generalization, not just fitting what it already saw.