Well, here we are! The end of the book. While you probably don’t have the same depth of understanding as a PhD in machine learning, I hope you have learned something. Specifically, I hope you’ve developed a thought process for approaching problems that machine learning works so well at solving. I firmly believe that using tests is the only way that we can effectively use the scientific method. It is the reason the modern world exists, and it helps us become much better at writing code.
Of course, you can’t write a test for everything, but it’s the mindset that matters. And hopefully you have learned a bit about how you can apply that mindset to machine learning. In this chapter, we will discuss what we covered at a high level, and I’ll post some suggested reading for you so you can dive further into machine learning research.
As we touched on earlier in the book, machine learning is a split into three main categories: supervised, unsupervised, and reinforcement learning (Table 11-1). This book skips reinforcement learning, but I highly suggest you research it now that you have a better background. I’ll list a source for you in the final section of this chapter.
Supervised learning is the most common machine learning category. This is functional approximation. We are trying to map some data points to some fuzzy function. Optimization ...