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
About the Author
About the Reviewers
Chapter Code Requirements

Appendix A. Chapter Code Requirements

This book's content leverages openly available data and code, including open source Python libraries and frameworks. While each chapter's example code is accompanied by a README file documenting all the libraries required to run the code provided in that chapter's accompanying scripts, the content of these files is collated here for your convenience.

It is recommended that you already have some libraries that are required for the earlier chapters when working with code from any later chapter. These requirements are identified using keywords. It is particularly important to set up the libraries mentioned in Chapter 1, Unsupervised Machine Learning, for any content provided later in the book. The requirements for every chapter are given in the following table:

Chapter Number



  • Python 3 (3.4 recommended)

  • sklearn (NumPy, SciPy)

  • matplotlib


  • theano


  • Semisup-learn


  • Natural Language Toolkit (NLTK)

  • BeautifulSoup


  • Twitter API account


  • XGBoost


  • Lasagne

  • TensorFlow