Linear regression is one of the most common techniques for modeling the relationship between continuous variables. The application of this method is very widely used in the industry. We started modeling part of the book on linear regression, not just because it's very popular, but because it's a relatively easy technique and contains most of the elements which almost every machine learning algorithm has.
In this chapter, we learned about supervised and unsupervised learning and built a linear regression model by using the Boston housing dataset. We touched upon different important concepts such as hyperparameters, loss functions, and gradient descent. The main purpose of this chapter was to give you sufficient knowledge so that you can build and tune a linear regression model and understand what it does step by step. We looked at two practical cases where we...
