For linear regression problems, people typically use a cost function that measures the distance between the linear model’s predictions and the training examples; the objective is to minimize this distance. This is where the Linear Regression algorithm comes in: you feed it your training examples and it finds the parameters that make the linear model fit best to your data. This is called training the model.
