Scikit-Learn Sprint

Welcome to our 2nd meetup, this time in collaboration with “Zürich Women in Machine Learning and Data Science”!

Once again, we will have the chance to contribute to scikit-learn, one of the most popular open-source libraries for machine learning!

We’ll have core developers from scikit-learn leading the sprint. As always, we welcome new contributors. For beginners in open-source, we will have a beginners’ table where you can make your first pull request on GitHub.

Please read the details below for more info on how to prepare for the event and what to expect during the evening.

This event has limited seats and may have a waiting list. If you’re confirmed but can’t attend, please remember to release your place to someone else. Similarly, please don’t show up if you’re on the waiting list but haven’t been confirmed. Unfortunately, we won’t be able to accommodate more people than planned.

If you are a member of “Zürich Women in Machine Learning and Data Science” and you cannot find a space here, you can join their event here instead.

Agenda

• 18.30: Welcome, networking, drinks and food
• 18.45: Sponsor presentation, scikit-learn presentation
• 19.00: Coding
• 21.30: End of the event, pub/drinks nearby for those who want to join

How to prepare for the sprint

You need to bring your own laptop and have a development environment already set up:

• Create the scikit-learn development environment following the instructions from steps 1 to 6
• (Optional) Extra videos resources are also available:
  • Crash Course in Contributing to Scikit-Learn & Open Source Projects: Video, Transcript
  • Example of Submitting a Pull Request to scikit-learn: Video, Transcript
  • Sprint-specific instructions and practical tips: Video, Transcript
  • 3 Components of Reviewing a Pull Request: Video, Transcript

First Time Contributors

• Create a GitHub account if you don’t have one.
• Install Python if you don’t have it already (for this sprint, we suggest using Miniconda or Anaconda).
• If you can, set up the development environment as shown above. If you experience any problems, we’ll help you fix them during the event.
• Check out the videos linked above to get familiar with the process of contributing to scikit-learn.

Code of Conduct

Please be reminded that all participants are expected to follow the NumFOCUS Code of Conduct

Thanks to Scigility for hosting this event!

As a start-up from the early days of the Big Data scene, we are enthusiastic about data and combine our unique experience with data, advanced analytics, and best practices to implement enterprise data platforms and AI/ML use cases. This and closely maintained partnerships with universities and technology providers enable us to be a leader in these areas on the Swiss and European markets. Our service portfolio includes data architecture and strategy consulting, data science and AI/ML use case implementation, data engineering, and platform engineering, as well as MLOps.

scikit-learn is an open source machine learning library that supports supervised and unsupervised learning. It also provides various tools for model fitting, data preprocessing, model selection, model evaluation, and many other utilities.

More information about Scikit-learn: https://scikit-learn.org/stable/

Set up instructions

Please follow the instruction in the scikit-learn contributing guide.