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+---
+title: Intro to Machine Learning with Sklearn (Carpentries)
+author: "Chris Endemann"
+date-modified: "last-modified"
+date-format: long
+image: "../../images/sklearn.png"
+
+categories:
+  - Intro ML
+  - Sklearn
+  - Workshop
+  - Carpentries
+  - Code-along
+---
+
+## About this external resource
+The [Intro to Machine Learning with Sklearn](https://carpentries-incubator.github.io/machine-learning-novice-sklearn/) workshop from the Carpentries will walk you through introductory machine learning concepts as well as how to implement common ML methods (e.g., regression, clustering, classication) using the popular scikit-learn ("sklearn") package. Sklearn makes it possible to quickly fit and evaluate many models in just a few lines of code. It also comes with convenient functions needed for nearly all ML pipelines (e.g., train/test split, gridsearchcv). **Note**: Don't use Sklearn for neural networks (it is the slowest option!). Instead, explore Keras or PyTorch.
+
+#### Prerequisites
+Learners are expected to have the following knowledge:
+
+- Basic Python programming skills and familiarity with the Pandas package. If you need a refresher, these [Introductory Python](https://swcarpentry.github.io/python-novice-gapminder/) lesson materials are available for independent study.
+
+#### Estimated time to complete
+This workshop takes approximately 8 hours to complete. 
+
+### Register to take this workshop in Madison!
+The Carpentries is a global organization of researchers who volunteer their time and effort to create workshops that teach software engineering and data analysis skills to other researchers. UW-Madison has its own [local Carpentries community](https://datascience.wisc.edu/uw-madison-carpentries-community/) which is actively engaged in developing new ML/AI workshops. To be notified of upcoming workshops offered by the Carpentries, make sure to subscribe to the [Data Science @ UW Newsletter](https://datascience.wisc.edu/newsletter/). The Intro Deep Learning workshop is typically taught in May each year.
+
+### Alternatively, work through the materials independently!
+All Carpentries lessons are published as open source educational materials. You are welcome and encouraged to visit the [lesson materials](https://carpentries-incubator.github.io/machine-learning-novice-sklearn/) to work through them on your own. If you are involved with a research lab at UW-Madison campus, you may attend [Coding Meetup](https://datascience.wisc.edu/hub/#dropin) (Tue/Thur, 2:30-4:30pm) to get help working through the materials.
+
+### See also
+- [**Workshop**: Intro to Deep Learning with Keras](https://uw-madison-datascience.github.io/ML-X-Nexus/Resources/Deep-learning_PyTorch/index.html): Once you master sklearn, start using Keras to build neural networks quickly.
+- [**Workshop**: Intro to Deep Learning with PyTorch](https://uw-madison-datascience.github.io/ML-X-Nexus/Resources/Deep-learning_PyTorch/index.html): Explore PyTorch as an alternative deep learning framework (faster but more verbose than Keras)
+- [**Book**: Understanding Deep Learning - Simon J.D. Prince](https://udlbook.github.io/udlbook/): This free textbook is a good modern overview of deep learning, and provides colab notebooks to explore deep learning concepts and implementations. The book uses PyTorch as its framework of choice. You may find additional details in this book that the workshop only briefly touches on.
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