Resources for setting up your coding environment

If you need help with opening the Jupyter notebooks, I made a short step by step guide that illustrates this process

  • A quick and great NumPy refresher that covers everything (and more) you'd need for this book

  • Recommended! To check your coding environment, open the check_environment.ipynb (it can be found in this directory) in Jupyter Notebook and execute the code cell:

Table of contents and code notebooks

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.

  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]

Bonus Notebooks (not in the book)


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.