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README.md

Scikit-Learn Workshop

Research Computing Services workshop materials for Scikit-learn.

Packages

This workshop requires packages: sklearn, pandas, numpy, pickle and matplotlib. Although not required, it would be great if you had the graphviz package.

Presentation

The presentation is available here.

Downloading Files

Recommended: Entire directory

You can download all of the files by clicking the green button above and choosing "Download ZIP."

Individual Files

If you download files from the links above, you have to click through to the RAW version of the notebook and download that. If you download directly from the links above, the files won't open because they are web pages, not the raw files.

Downloading Exercises

To download just the exercise files, right-click on the links below, and choose Save Link As (or the similar option in your browser). Make sure to choose All file types as the content type (or .ipynb if available), and remove any .txt or similar extensions from the file when you save it. The files should be *.ipynb files, with no additional file type extensions.

Exercises WITHOUT Answers

Exercises WITH Answers

Additional Exercises

RCS Stats models and scikit-learn Workshop 2017 by Christina Maimone

Machine learning with scikit-learn

Resources

See Resources for a listing of general Python resources, tutorials, and reference materials. Links below relate specifically to material covered in this workshop.

Scikit-Learn Cheat Sheet: common models and steps

Machine Learning with Scikit-Learn: videos, notebooks, and Kaggle blog posts covering the basic models and ideas behind them

Model evaluation, model selection, and algorithm selection in machine learning: this is the first in a three part series of good explanations/tutorials for those looking to better understand how to navigate options in machine learning; written by Sebastian Raschka, a computational biologist (but the material is for a general audience)

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