#scikitlearn #MachineLearning #OpenSource scikit-learn co-founder Gaël Varoquaux and @JonKrohnLearns 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 (https://gurobi.com/sds), the Decision Intelligence Leader, by Data Universe (https://datauniverse2024.com), the out-of-this-world Data Conference, and by CloudWolf (https://www.cloudwolf.com/sds), the Cloud Skills platform. Interested in sponsoring a SuperDataScience Podcast episode? Visit https://jonkrohn.com/podcast for sponsorship information. In this episode you will learn: • [00:00:00] Introduction • [00:03:39] The early beginnings and growth of scikit-learn • [00:16:10] Development principles of scikit-learn • [00:19:21] How to apply scikit-learn to your ML problem • [00:23:37] Resource-efficiency and scikit-learn development • [00:36:26] How to contribute to an open-source project like scikit-learn yourself • [00:49:18] The future of scikit-learn • [01:00:37] Gaël on the social-impact data projects in his Soda lab • [01:09:28] Why domain expertise and statistical rigor are more important than ever Additional materials: https://www.superdatascience.com/737