In the previous chapters, I stressed the importance of understanding the mathematics behind algorithms. Here's a recap. We started with linear regression, followed by a Naïve Bayes classifier. Then, the topics dovetailed into one of the more complex topics in data science: time series. We then detoured and discussed clustering by means of K-means. This was followed by two chapters on neural networks. In all these chapters, I explained the mathematics behind these algorithms, and showed that, with much surprise, the programs yielded are short and simple.
The purpose of this book is to walk a delicate line between the math and the implementations. I hope I have provided enough information so that you have an understanding of the mathematics and how they may be useful. The projects are real projects, but often they are in...
