Sebastian Raschka, 2015

Python Machine Learning - Code Examples

## Bonus Material

A collection of additional notebooks and code examples to clarify and explain concepts based on reader feedback.


- A Basic Pipeline and Grid Search Setup [[GitHub ipynb](./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)]

- An Extended Nested Cross-Validation Example [[GitHub ipynb](./nested_cross_validation.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/nested_cross_validation.ipynb)]

- A Simple(r) Barebones Flask Webapp Template [[view directory](./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](./reading_mnist.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/reading_mnist.ipynb)]

- Scikit-learn Model Persistence using JSON [[GitHub ipynb](./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)]

- Multinomial logistic regression / softmax regression [[GitHub ipynb](./softmax-regression.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/bonus/softmax-regression.ipynb)]
