Book Image

Advanced Machine Learning with Python

Book Image

Advanced Machine Learning with Python

Overview of this book

Designed to take you on a guided tour of the most relevant and powerful machine learning techniques in use today by top data scientists, this book is just what you need to push your Python algorithms to maximum potential. Clear examples and detailed code samples demonstrate deep learning techniques, semi-supervised learning, and more - all whilst working with real-world applications that include image, music, text, and financial data. The machine learning techniques covered in this book are at the forefront of commercial practice. They are applicable now for the first time in contexts such as image recognition, NLP and web search, computational creativity, and commercial/financial data modeling. Deep Learning algorithms and ensembles of models are in use by data scientists at top tech and digital companies, but the skills needed to apply them successfully, while in high demand, are still scarce. This book is designed to take the reader on a guided tour of the most relevant and powerful machine learning techniques. Clear descriptions of how techniques work and detailed code examples demonstrate deep learning techniques, semi-supervised learning and more, in real world applications. We will also learn about NumPy and Theano. By this end of this book, you will learn a set of advanced Machine Learning techniques and acquire a broad set of powerful skills in the area of feature selection & feature engineering.
Table of Contents (17 chapters)
Advanced Machine Learning with Python
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Chapter Code Requirements
Index

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 and being...