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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.

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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

Excerpts from the Foreword and Preface.

  1. Machine Learning - Giving Computers the Ability to Learn from Data [./code/ch01] [ipynb]
  2. Training Machine Learning Algorithms for Classification [./code/ch02] [ipynb]
  3. A Tour of Machine Learning Classifiers Using Scikit-Learn [./code/ch03] [ipynb]
  4. Building Good Training Sets – Data Pre-Processing [./code/ch04] [ipynb]
  5. Compressing Data via Dimensionality Reduction
  6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization
  7. Combining Different Models for Ensemble Learning
  8. Applying Machine Learning to Sentiment Analysis
  9. Embedding a Machine Learning Model into a Web Application
  10. Predicting Continuous Target Variables with Regression Analysis
  11. Working with Unlabeled Data – Clustering Analysis
  12. Training Artificial Neural Networks for Image Recognition
  13. Parallelizing Neural Network Training via Theano

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Python Machine Learning code repository

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  • TeX 83.4%
  • Python 16.6%