scikit-learn Open-Source Sprint [Beginners Welcome]

Welcome to the 1st sprint of Python Sprints Zürich!

In our first meetup, we'll have the opportunity to contribute to scikit-learn, one of the most popular open-source libraries for machine learning!

We'll have core developers from scikit-learn lead the sprint. As always, we welcome new contributors. For beginners in open-source, we'll have a beginners' table for making 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 are 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'll not be able to accommodate more people than planned.

In case of a waiting list, we will give priority to: people from underrepresented groups in tech, maintainers of open-source libraries or supporting members of NumFOCUS.

SPONSOR ✨

A huge thanks to Scigility for sponsoring this event!

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 if you want to learn more about how to contribute to Scikit-Learn.

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.