Merge pull request #207 from EdAbati/add-zurich-chapter

Added Zurich chapter

Author: EdAbati

_chapters/zurich_python_sprints.md

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+---
+category: "zurich"
+title: "Zürich Python Sprints"
+meetup_link: https://www.meetup.com/Python-Sprints/
+address: Zürich, Switzerland
+country_code: ch
+image: static/images/chapters/london_python_sprints_1920x600px.jpg
+lat: 47.378976
+lng: 8.532275
+sponsors:
+  - scigility
+---
+We are a group of programmers based in Zürich who are passionate about making open-source projects better.
+We believe in sharing our skills for free to earn our good karma. If you are devoted to a particular open source project, please let us know, we could make a sprint dedicated to it!
+Inclusion is in the nature of our group and we want to make sure that no one is underrepresented. All people are welcome.
+Our group was founded in February 2023.

_posts/2023-03-28-scikit-learn-sprint.md

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+---
+category: "zurich"
+title: "Scikit-Learn Sprint"
+level: "All"
+time: "18:30"
+rsvp_link: https://www.meetup.com/python-sprints-zurich/events/291718007/
+event_link:
+project: scikit-learn
+image:
+sponsor: scigility
+---
+
+Welcome to the first sprint of Python Sprints Zürich!
+
+In our first meetup, we'll have the opportunity 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 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.
+
+
+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)

_projects/scikit-learn.md

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+---
+obj_id: scikit-learn
+name: "Scikit-learn"
+logo: static/images/projects/scikit-learn.png
+website: https://scikit-learn.org/stable/
+setup_html: |
+    <p>
+        Please follow the instruction in the 
+        <a href="https://scikit-learn.org/stable/developers/contributing.html">scikit-learn contributing guide</a>.
+    </p>
+---
+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.

_sponsors/scigility.md

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+---
+obj_id: scigility
+name: "Scigility"
+logo: static/images/sponsors/scigility.png
+link: https://scigility.com
+address: "Europaallee 41, 8004 Zürich, Switzerland"
+lat: 47.378841
+lng: 8.532023
+---
+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.