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PyParis 2018: Machine learning using scikit-learn

Instructors

  • Guillaume Lemaitre - Inria, Paris-Saclay Center for Data Science
  • Jeremie du Boisberranger - scikit-learn @ fondation Inria
  • Joris Van den Bossche - Inria, Paris-Saclay Center for Data Science
  • and others to come ...

Obtaining the Tutorial Material

If you have a GitHub account, it is probably most convenient if you clone or fork the GitHub repository. You can clone the repository by running:

git clone https://github.com/glemaitre/pyparis-2018-sklearn.git

If you are not familiar with git or don't have an GitHub account, you can download the repository as a .zip file by heading over to the GitHub repository (https://github.com/glemaitre/pyparis-2018-sklearn) in your browser and click the green “Download” button in the upper right.

Please note that we may add and improve the material until shortly before the tutorial session, and we recommend you to update your copy of the materials one day before the tutorials. If you have an GitHub account and cloned the repository via GitHub, you can sync your existing local repository with:

git pull origin master

If you don't have a GitHub account, you may have to re-download the .zip archive from GitHub.

Requirements

To check if your system have the required libraries, you can execute the following script:

python check_environment.py

If you are using conda, you can create a specific environment for this tutorial with the following commands:

conda env create environment.yml
conda activate pyparis_sklearn  # or source activate pyparis_sklearn

References

This material is based on the fruitful work of the scikit-learn community and more broadly to the Pythonista of the whole data science community.

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PyParis tutorial on machine learning using scikit-learn

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