Main concepts behind Machine Learning

Imagine you are teaching a kid to differentiate dogs from cats: at first, you show him many images of both animals, identifying each of them. With these examples, he can associate each animal with its name and then classify new images correctly. The supervised learning has exactly the same idea: from a big train dataset, the algorithm "learns" the relationship between data and label and, therefore, it can predict the result of any other input. In mathematical terms, we are trying to find a expression Y f(X) b that can predict the results. Where X is the input, Y is the prediction and f(X) b is the model learned by the algorithm.