Name: scikit-learn Open-Source Sprint [Beginners Welcome]
Start: 2023-11-07T18:30:00+01:00
End: 2023-11-07T21:30:00+01:00
Location: OBC Suisse AG

Welcome to our 2nd meetup, this time in collaboration with "[Zürich Women in Machine Learning and Data Science](https://www.meetup.com/zurich-women-in-machine-learning-and-data-science/)"!

Once again, we will have the chance to contribute to **scikit-learn**, one of the [most popular](https://www.kaggle.com/kaggle-survey-2022) 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.

If you are a member of "[Zürich Women in Machine Learning and Data Science](https://www.meetup.com/zurich-women-in-machine-learning-and-data-science/)" and you cannot find a space here, you can join their event [here](https://www.meetup.com/zurich-women-in-machine-learning-and-data-science/events/296446850/) instead.

**SPONSOR ✨**

A huge thanks to [Scigility](https://scigility.com) 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](https://scikit-learn.org/dev/developers/contributing.html#how-to-contribute) from steps 1 to 6
* (Optional) [Extra videos resources](https://scikit-learn.org/dev/developers/contributing.html#video-resources) are also available if you want to learn more about how to contribute to Scikit-Learn.

***First Time Contributors***

* Create a [GitHub account](https://github.com) if you don't have one.
* Install Python if you don't have it already (for this sprint, we suggest using [Miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html) or [Anaconda](https://docs.anaconda.com/anaconda/install/index.html)).
* 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](https://numfocus.org/code-of-conduct).

Edoardo Abati

Florian R

Python Sprints Zürich (now PyData Zurich)

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scikit-learn Open-Source Sprint [Beginners Welcome]

OBC Suisse AG

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Python Sprints Zürich (now PyData Zurich)

scikit-learn Open-Source Sprint [Beginners Welcome]

Python Sprints Zürich (now PyData Zurich)

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