Now in contrast, supervised learning is a case where we have a set of answers that the model can learn from. We give it a set of training data, that the model learns from. It can then infer relationships between the features and the categories that we want, and apply that to unseen new values - and predict information about them.

Going back to our earlier example, where we were trying to predict car prices based on the attributes of those cars. That's an example where we are training our model using actual answers. So I have a set of known cars and their actual prices that they sold for. I train the model on that set of complete answers, and then I can create a model that I'm able to use to predict the prices of new cars that I haven't seen before. That's an example of supervised learning, where you're giving it a set of answers to learn from. You've already assigned categories or some organizing criteria to a set of data, and your algorithm then uses that criteria to build a model from which it can predict new values.