Machine Learning
A way for computers to learn from examples instead of being given exact rules — by finding patterns in labelled or unlabelled data.
Plain English Explanation
Machine learning is how computers learn from examples instead of being told exact rules. You show the system many inputs paired with correct answers, and it gradually figures out the patterns on its own.
Once trained, the model can apply what it learned to new situations it has never seen before.
Analogy
Machine learning is like learning to identify birds by looking at thousands of photographs with names attached, rather than memorising a field guide written by an expert. Eventually you start recognising patterns — shape, colour, size — without anyone spelling out every rule.
How is it used?
Netflix learns what you might want to watch from your viewing history. Email apps learn which messages are spam from millions of labelled examples. Voice assistants improve at recognising your speech the more people use them.
Real-world Example
Credit scoring, product recommendations, and medical risk prediction are everyday applications built on machine learning.
Common Misconceptions
Machine learning is not magic — poor data, biased examples, or the wrong problem setup can produce models that look impressive in tests but fail in practice.
See Also
Also known as: ML