README.md · 杨雪元/introduction_to_ml_with_python2

[![Binder](https://mybinder.org/badge.svg)](https://mybinder.org/v2/gh/amueller/introduction_to_ml_with_python/master)

# 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 books 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 requirs ``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)

杨雪元/introduction_to_ml_with_python2

简介

发行版

贡献者

近期动态

杨雪元/introduction_to_ml_with_python2 .gitee-modal { width: 500px !important; }

简介

发行版

贡献者

近期动态

搜索帮助

杨雪元/introduction_to_ml_with_python2