Supervised Learning
In simple words
Learning a mapping from labelled examples. The workhorse of applied ML.
The fuller explanation
You have inputs and you have the answers. Supervised learning fits a function between them and hopes it generalises to inputs it has never seen.
Two shapes cover nearly everything: classification predicts a category, regression predicts a number. The distinction matters less than it seems, because the same models usually handle both with a different output layer and loss function.
What actually determines success is rarely the model. It is the quality of the labels, whether your training distribution resembles production, and whether your evaluation split is honest. A logistic regression on clean, well-split data beats a transformer on leaky data every time, and the second failure is invisible until it reaches users.
Learn these first
Real prerequisites, taken from the map rather than guessed.
Sources
Where this came from, so you can go past us.
Where does this sit on your route?
The free assessment places you on the same map and names which terms stand between you and the role you want.
Take the free assessmentSee it in context
The Atlas shows this term with everything that leads into it and everything that follows, as one picture.
Open the map