In this chapter, we've studied linear regression and a couple of algorithms that can be used to formulate an optimal linear regression model from some sample data. The following are some of the other points that we covered:
We discussed linear regression with single and multiple variables
We implemented the gradient descent algorithm to formulate a linear regression model with one variable
We implemented the Ordinary Least Squares (OLS) method to find the coefficients of an optimal linear regression model
We introduced regularization and how it could be applied to linear regression
In the following chapter, we will study a different area of machine learning, that is, classification. Classification is also a form of regression and is used to categorize data into different classes or groups.