add zurich event 2023-11-07 (#212)

Author: EdAbati

_posts/2023-11-07-scikit-learn-sprint.md

@@ -0,0 +1,56 @@
+---
+category: "zurich"
+title: "Scikit-Learn Sprint"
+level: "All"
+time: "18:30"
+rsvp_link: https://www.meetup.com/python-sprints-zurich/events/296536487/
+event_link:
+project: scikit-learn
+image:
+sponsor: scigility
+---
+
+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`](https://scikit-learn.org), 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 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](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.
+
+
+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:
+    - Crash Course in Contributing to Scikit-Learn & Open Source Projects: [Video](https://youtu.be/5OL8XoMMOfA), [Transcript](https://github.com/data-umbrella/event-transcripts/blob/main/2020/05-andreas-mueller-contributing.md)
+    - Example of Submitting a Pull Request to scikit-learn: [Video](https://youtu.be/PU1WyDPGePI), [Transcript](https://github.com/data-umbrella/event-transcripts/blob/main/2020/06-reshama-shaikh-sklearn-pr.md)
+    - Sprint-specific instructions and practical tips: [Video](https://youtu.be/p_2Uw2BxdhA), [Transcript](https://github.com/data-umbrella/data-umbrella-scikit-learn-sprint/blob/master/3_transcript_ACM_video_vol2.md)
+    - 3 Components of Reviewing a Pull Request: [Video](https://youtu.be/dyxS9KKCNzA), [Transcript](https://github.com/data-umbrella/event-transcripts/blob/main/2021/27-thomas-pr.md)
+
+### 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)