python-machine-learning-book

Python Machine Learning code repository.

What you can expect are 400 pages rich in useful material just about everything you need to know to get started with machine learning ... from theory to the actual code that you can directly put into action! This is not yet just another "this is how scikit-learn works" book. I aim to explain all the underlying concepts, tell you everything you need to know in terms of best practices and caveats, and we will put those concepts into action mainly using NumPy, scikit-learn, and Theano.

You are not sure if this book is for you? Please checkout the excerpts from the Foreword and Preface, or take a look at the FAQ section for further information.

Links

• ebook and paperback at Amazon.com, Amazon.co.uk, Amazon.de
• ebook and paperback from Packt (the publisher)
• at other book stores: Google Play Store, O'Reilly, Safari, Barnes & Noble,
• ebook at Apple iBooks
• A sample chapter

Contact

I am happy to answer questions! Just write me an email or consider asking the question on the Google Groups Email List.

If you are interested in keeping in touch, I have quite a lively twitter stream (@rasbt) all about data science and machine learning. I also maintain a blog where I post all of the things I am particularly excited about.

Table of Contents

Simply click on the ipynb/nbviewer links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by the NbViewer version). Please note that these are just the code examples accompanying the book, which I uploaded for your convenience; be aware that these notebooks may not be useful without the formulae and descriptive text.

Excerpts from the Foreword and Preface

1. Machine Learning - Giving Computers the Ability to Learn from Data [dir] [ipynb] [nbviewer]
2. Training Machine Learning Algorithms for Classification [dir] [ipynb] [nbviewer]
3. A Tour of Machine Learning Classifiers Using Scikit-Learn [dir] [ipynb] [nbviewer]
4. Building Good Training Sets – Data Pre-Processing [dir] [ipynb] [nbviewer]
5. Compressing Data via Dimensionality Reduction [dir] [ipynb] [nbviewer]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [dir] [ipynb] [nbviewer]
7. Combining Different Models for Ensemble Learning [dir] [ipynb] [nbviewer]
8. Applying Machine Learning to Sentiment Analysis [dir] [ipynb] [nbviewer]
9. Embedding a Machine Learning Model into a Web Application [dir] [ipynb] [nbviewer]
10. Predicting Continuous Target Variables with Regression Analysis [dir] [ipynb] [nbviewer]
11. Working with Unlabeled Data – Clustering Analysis [dir] [ipynb] [nbviewer]
12. Training Artificial Neural Networks for Image Recognition [dir] [ipynb] [nbviewer]
13. Parallelizing Neural Network Training via Theano [dir] [ipynb] [nbviewer]

Literature References & Further Reading Resources

Image Gallery

Errata

FAQ

• What do other people think about this book?
• How is this different from other machine learning books?
• Which version of Python was used in the code examples?
• Which technologies and libraries are being used?
• Which book version/format would you recommend?
• Why did you choose Python for machine learning?
• Why do you use so many leading and trailing underscores in the code examples?
• Are There Any Prerequisites and Recommended Pre-Readings?