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.
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