What is Machine Learning?

Show examples, adjust, repeat

Machine learning is the learned-rules approach made concrete. Instead of writing a rule, you give the system examples with the right answer attached — photos labeled cat or dog, emails labeled spam or not — and let it work out the pattern.

The system starts as a large set of adjustable numbers that produce bad guesses. Training shows it an example, compares its guess with the label, and nudges every number slightly in the direction that would have made the guess less wrong. Repeat that across millions of examples and the guesses stop being bad.

Two things follow from this. A model can only be as good as its examples: if the training data never shows a case, the model has no reliable basis for it. And the finished model holds no readable rule. The pattern is spread across all those numbers, which is why a model can be right without anyone being able to say exactly why.