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Notebooks and code for the book "Introduction to Machine Learning with Python"
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| 2026-07-31 | 8130 |
| 2026-08-02 | 8130 |
| 2026-08-06 | 8134 |
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[](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 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".
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matched fp:d20743a063f22281, llm:Description: 'Notebooks and code for the book "Introduction to Machine Learning with Python"'
matched fp:d20743a063f22281, llm:Description: 'Notebooks and code for the book "Introduction to Machine Learning with Python"'