scikit-learn co-founder Gaël Varoquaux and Jon Krohn are live at the historic Sorbonne in Paris, where they discuss the evolution of scikit-learn. From its origins as a memory-efficient Python implementation of support vector machines to its present-day status as a pivotal resource in machine learning, Gaël paints a vivid picture of its remarkable growth. Join us for a glimpse into scikit-learn's evolution, the realm of open-source collaboration, and the transformative power of data-driven insights in today's dynamic data landscape.

This episode is brought to you by Gurobi (gurobi.com/sds), the Decision Intelligence Leader, by Data Universe (https://datauniverse2024.com), the out-of-this-world data conference, and by CloudWolf (www.cloudwolf.com/sds), the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit JonKrohn.com/podcast for sponsorship information.

In this episode you will learn:
• The early beginnings and growth of scikit-learn [05:34]
• Development principles of scikit-learn [18:05]
• How to apply scikit-learn to your ML problem [21:16]
• Resource-efficiency and scikit-learn development [25:32]
• How to contribute to an open-source project like scikit-learn yourself [38:21]
• The future of scikit-learn [51:13]
• Gaël on the social-impact data projects in his Soda lab [1:02:33]
• Why domain expertise and statistical rigor are more important than ever [1:11:24]

Additional materials: www.superdatascience.com/737