Scikit-learn Sprint (to contribute to open source)

This is a past event

63 people went

Price: $5.00
Location image of event venue


9:30am - 10:00am: arrive early for technical support
10:00am - 1:00pm: Sprint
1:00pm - 2:00pm: Lunch will be provided
2:00pm - 4:00pm: Sprint

Code of Conduct
WiMLDS is dedicated to providing a harassment-free experience for everyone. We do not tolerate harassment of participants in any form. All communication should be appropriate for a professional audience including people of many different backgrounds. Sexual language and imagery is not appropriate.

All attendees should read the full Code of Conduct before participating:

We would like to increase the number of women in open source, particularly for the Python machine learning library scikit-learn. The scikit-learn repository ( has over 1000 issues open. By organizing and offering this workshop, we hope to increase women’s participation in open source as well as advance the scikit-learn library.

The plan is to work in pairs. The goal is that each participant will be able to resolve one trivial fix and one actual fix.

GitHub Repo

We will review the basics of Git at the beginning of workshop so attendees are able to submit pull requests.

1) Hardware
- bring your laptop & charger

2) Software
- have Python installed via Anaconda. (Anaconda includes Jupyter Notebook)

3) Python / R
- be comfortable with Python
- familiarity with Jupyter Notebook

R users are also welcome and able to contribute:
- your math knowledge
- help with documentation
- good idea to look at sphinx and ReST

4) Git
- Git should be installed
- should have a GitHub account (save your password where you can find it)
- some familiarity with Git / GitHub
- review some Git resources prior to event
- we'll go over pull requests at beginning of event

1) Read thru "Contributing" documentation
- it is approximately 16 pages

2) Review Open Issues
go through some Issues and become familiar with them

Instructor Bio
This event will be led by Andreas Mueller. Andreas is a Lecturer in Data Science at Columbia University. Previously he worked as a Machine Learning Scientist at Amazon, working on computer vision and forecasting problems. He is one of the core developers of the scikit-learn machine learning library, and has maintained it for several years.
His mission is to create open tools to lower the barrier of entry for machine learning applications, promote reproducible science and democratize the access to high-quality machine learning algorithms.

Andreas’ twitter: @amuellerml
Andreas’ github: @amueller