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.

Sebastian Raschka’s new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it’s just as I expected - really great! It’s well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.
– Lon Riesberg at Data Elixir

Superb job! Thus far, for me it seems to have hit the right balance of theory and practice…math and code!
– Brian Thomas

I've read (virtually) every Machine Learning title based around Scikit-learn and this is hands-down the best one out there.
– Jason Wolosonovich

• ebook and paperback at Amazon.com, Amazon.co.uk, Amazon.de
• ebook and paperback from Packt (the publisher)
• at other book stores: Google Books, O'Reilly, Safari, Barnes & Noble, Apple iBooks, ...
• social platforms: Goodreads

• German translation via “mitp Verlag”
• Japanese translation via “Impress Top Gear”

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
• Instructions for setting up Python and the Jupiter Notebook

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] [