In Chapter 1, Getting Started with Regression, we said that regression models are the most well-understood in numerical simulation. Once we've acquired the necessary experience in regression models, we will be able to understand all other machine learning algorithms. Regression models are easily interpretable as they are based on solid mathematical bases. Perhaps this is the reason behind the extreme ease of understanding of such techniques.
After analyzing in detail the different regression algorithms, it is time to put them into practice. This last chapter is meant to be a short reference, covering some of the major regression algorithms. In this chapter, you will just apply what has been learned. You can explore multiple linear regression, logistic regression, random forest regression, neural networks, and much more as applied to datasets...
