Summary
You've learned a lot in this section – or had a brief recap, at least. You are now fresh on the concepts of machine learning, regression, classification, and automation. All of these are crucial for the following, more demanding sections.
The chapters after the next one will dive deep into the code, so you will get a full grasp of the library. Everything from the most basic regression and classification automation, to parallel training, neural networks, and model deployment will be discussed.
In the next chapter, we'll dive deep into the TPOT library, its use cases, and its underlying architecture. We will discuss the core principle behind TPOT – genetic programming – and how is it used to solve regression and classification tasks. We will also fully configure the environment for the Windows, macOS, and Linux operating systems.
