July 22, 2019

548 words 3 mins read



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

repo name amueller/introduction_to_ml_with_python
repo link https://github.com/amueller/introduction_to_ml_with_python
language Jupyter Notebook
size (curr.) 169394 kB
stars (curr.) 4263
created 2016-05-29


Introduction to Machine Learning with Python

This repository holds the code for the forthcoming book “Introduction to Machine Learning with Python” by Andreas Mueller and Sarah Guido. You can find details about the book on the O’Reilly website.

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.

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


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

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.


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.

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. 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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