Chapter 9. Additional Python Machine Learning Tools

Over the course of the eight preceding chapters, we have examined and applied a range of techniques that help us enrich and model data for many applications.

We approached the content in these chapters using a combination of Python libraries, particularly NumPy and Theano, while the other libraries were drawn upon as and when we needed to access specific algorithms. We did not spend a great deal of time discussing what other options existed in terms of tools, what the unique differentiators of these tools were, or why we might be interested.

The primary goal of this final chapter is to highlight some other key libraries and frameworks that are available to you to use. These tools streamline and simplify the process of creating and applying models. This chapter presents these tools, demonstrates their application, and provides extensive advice regarding Further reading.

A major contributor to succeed in solving data science challenges...