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https://github.com/amueller/introduction_to_ml_with_python

Notebooks and code for the book "Introduction to Machine Learning with Python"
https://github.com/amueller/introduction_to_ml_with_python

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Notebooks and code for the book "Introduction to Machine Learning with Python"

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# Introduction to Machine Learning with Python



This repository holds the code for the forthcoming book "Introduction to Machine

Learning with Python" by [Andreas Mueller](http://amueller.io) and [Sarah Guido](https://twitter.com/sarah_guido).

You can find details about the book on the [O'Reilly website](http://shop.oreilly.com/product/0636920030515.do).



The book requires the current stable version of scikit-learn, that is

0.20.0.  Most of the book can also be used with previous versions of

scikit-learn, though you need to adjust the import for everything from the

``model_selection`` module, mostly ``cross_val_score``, ``train_test_split``

and ``GridSearchCV``.



This repository provides the notebooks from which the book is created, together

with the ``mglearn`` library of helper functions to create figures and

datasets.



For the curious ones, the cover depicts a [hellbender](https://en.wikipedia.org/wiki/Hellbender).



All datasets are included in the repository, with the exception of the aclImdb dataset, which you can download from

the page of [Andrew Maas](http://ai.stanford.edu/~amaas/data/sentiment/). See the book for details.



If you get ``ImportError: No module named mglearn`` you can try to install mglearn into your python environment using

the command ``pip install mglearn`` in your terminal or ``!pip install mglearn`` in Jupyter Notebook.



## Errata

Please note that the first print of the book is missing the following line when listing the assumed imports:



```python

from IPython.display import display

```

Please add this line if you see an error involving ``display``.



The first print of the book used a function called ``plot_group_kfold``.

This has been renamed to ``plot_label_kfold`` because of a rename in

scikit-learn.



## Setup



To run the code, you need the packages ``numpy``, ``scipy``, ``scikit-learn``, ``matplotlib``, ``pandas`` and ``pillow``.

Some of the visualizations of decision trees and neural networks structures also require ``graphviz``. The chapter

on text processing also requires ``nltk`` and ``spacy``.



The easiest way to set up an environment is by installing [Anaconda](https://www.continuum.io/downloads).



### Installing packages with conda:

If you already have a Python environment set up, and you are using the ``conda`` package manager, you can get all packages by running



    conda install numpy scipy scikit-learn matplotlib pandas pillow graphviz python-graphviz



For the chapter on text processing you also need to install ``nltk`` and ``spacy``:



    conda install nltk spacy



### Installing packages with pip

If you already have a Python environment and are using pip to install packages, you need to run



    pip install numpy scipy scikit-learn matplotlib pandas pillow graphviz



You also need to install the graphiz C-library, which is easiest using a package manager.

If you are using OS X and homebrew, you can ``brew install graphviz``. If you are on Ubuntu or debian, you can ``apt-get install graphviz``.

Installing graphviz on Windows can be tricky and using conda / anaconda is recommended.

For the chapter on text processing you also need to install ``nltk`` and ``spacy``:



    pip install nltk spacy



### Downloading English language model

For the text processing chapter, you need to download the English language model for spacy using



    python -m spacy download en



## Submitting Errata



If you have errata for the (e-)book, please submit them via the [O'Reilly Website](http://www.oreilly.com/catalog/errata.csp?isbn=0636920030515).

You can submit fixes to the code as pull-requests here, but I'd appreciate it if you would also submit them there, as this repository doesn't hold the

"master notebooks".



![cover](cover.jpg)