Python AI: How to Build a Neural Network & Make Predictions – Real Python

In basic terms, the goal of using AI is to make computers think as humans do. This may seem like something new, but the field was born in the 1950s.

A common machine learning task is supervised learning , in which you have a dataset with inputs and known outputs. The task is to use this dataset to train a model that predicts the correct outputs based on the inputs. The image below presents the workflow to train a model using supervised learning:

The goal of supervised learning tasks is to make predictions for new, unseen data. To do that, you assume that this unseen data follows a probability distribution similar to the distribution of the training dataset. If in the future this distribution changes, then you need to train your model again using the new training dataset.