In this chapter, we introduced linear regression as a supervised machine learning algorithm. We explained its functional form, its relationship with the statistical measures of mean and correlation, and we tried to build a simple linear regression model on the Boston house prices data. After doing that we finally glanced at how regression works under the hood by proposing its key mathematical formulations and their translation into Python code.
In the next chapter, we will continue our discourse about linear regression, extending our predictors to multiple variables and carrying on our explanation where we left it suspended during our initial illustration with a single variable. We will also point out the most useful transformations you can apply to data to make it suitable for processing by a linear regression algorithm.
