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jirkalhotka Dec 17, 2018
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Expand Up @@ -49,28 +49,54 @@ Russian ISBN-13: 978-5970604090<br>
## 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).
Simply click on the `ipynb`/`nbviewer`/[`Deepnote`](https://beta.deepnote.com) links next to the chapter headlines to view the code examples (currently, the internal document links are only supported by NbViewer and Deepnote).
**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](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf)
- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md)
- [Instructions for setting up Python and the Jupiter Notebook](./code/ch01/README.md)

<br>

1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch01%2Fch01.ipynb)

2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch02%2Fch02.ipynb)

3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch03%2Fch03.ipynb)

4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch04%2Fch04.ipynb)

5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)]
6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch05%2Fch05.ipynb)

6. Learning Best Practices for Model Eval. and Hyperparameter Opt. [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch06%2Fch06.ipynb)

7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch07%2Fch07.ipynb)

8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch08%2Fch08.ipynb)

9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch09%2Fch09.ipynb)

10. Predicting Continuous Target Variables with Regression Analysis [[dir](./code/ch10)] [[ipynb](./code/ch10/ch10.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch10/ch10.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch10%2Fch10.ipynb)

11. Working with Unlabeled Data – Clustering Analysis [[dir](./code/ch11)] [[ipynb](./code/ch11/ch11.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch11/ch11.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch11%2Fch11.ipynb)

12. Training Artificial Neural Networks for Image Recognition [[dir](./code/ch12)] [[ipynb](./code/ch12/ch12.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch12/ch12.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch12%2Fch12.ipynb)

13. Parallelizing Neural Network Training via Theano [[dir](./code/ch13)] [[ipynb](./code/ch13/ch13.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch13/ch13.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fch13%2Fch13.ipynb)


<br>

Expand Down Expand Up @@ -194,12 +220,24 @@ If you need help to decide whether this book is for you, check out some of the "
### Bonus Notebooks (not in the book)

- Logistic Regression Implementation [[dir](./code/bonus)] [[ipynb](./code/bonus/logistic_regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/logistic_regression.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Flogistic_regression.ipynb)

- A Basic Pipeline and Grid Search Setup [[dir](./code/bonus)] [[ipynb](./code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/svm_iris_pipeline_and_gridsearch.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fsvm_iris_pipeline_and_gridsearch.ipynb)

- An Extended Nested Cross-Validation Example [[dir](./code/bonus)] [[ipynb](./code/bonus/nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fnested_cross_validation.ipynb)

- A Simple Barebones Flask Webapp Template [[view directory](./code/bonus/flask_webapp_ex01)][[download as zip-file](https://github.com/rasbt/python-machine-learning-book/raw/master/code/bonus/flask_webapp_ex01/flask_webapp_ex01.zip)]
- Reading handwritten digits from MNIST into NumPy arrays [[GitHub ipynb](./code/bonus/reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Freading_mnist.ipynb)

- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./code/bonus/scikit-model-to-json.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/scikit-model-to-json.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fscikit-model-to-json.ipynb)

- Multinomial logistic regression / softmax regression [[GitHub ipynb](./code/bonus/softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
[<img align="right" height="24" src="https://beta.deepnote.com/buttons/launch-in-deepnote.svg">](https://beta.deepnote.com/launch?template=data-science&url=https%3A%2F%2Fgithub.com%2Frasbt%2Fpython-machine-learning-book%2Fblob%2Fmaster%2Fcode%2Fbonus%2Fsoftmax-regression.ipynb)


<hr>

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