Machine learning from examples that come paired with the right answers.
Supervised learning teaches an AI with examples that carry answer labels: "this photo is a cat", "this email is spam". The AI makes a guess, compares it with the label, and adjusts its numbers by how wrong it was. Sorting photos, filtering spam and reading handwriting are classic uses.
The catch: labeling takes a lot of human work. So it's used alongside unsupervised learning, which finds patterns without labels, and self-supervised learning, which makes its own quiz questions from raw data. Large language models first learn self-supervised, guessing hidden next words, then get polished with supervised learning on answers written by people.
Think of it like this
It's like studying with a workbook that has an answer key. You do a problem, check the key, and fix what you got wrong. Making a good answer key takes effort, which is why good workbooks are precious.
For example
Show an AI thousands of photos labeled "dog" or "cat", and it learns to sort new photos it has never seen.