Open source library for machine learning in Python.

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This blog post has been submitted by Probabl, a sponsor of scikit-learn. The scikit-learn project values educational efforts that build and nurture a strong vibrant open-source community. The goal of this is straightforward: give everyone, everywhere, the tools they need to easily grasp, engage with, and meaningfully contribute to data science using open-source software. This mission is shared and actively supported by Probabl, a company that helps maintain scikit-learn by employing many of its core contributors and investing in its long-term sustainability. With Probabl’s support and a deep commitment from the community, the scikit-learn ecosystem continues building bridges between research, software, and education.

When the Inria scikit-learn MOOC (Massive Open Online Course) first went live, the community got a front-row seat to the amazing impact of practical, accessible and open learning. Created by several core developers and maintainers of scikit-learn—now working at Probabl—the MOOC has reached over 40,000 learners worldwide, clearly highlighting the demand for organized, hands-on resources that blend theory with real-world practice.

Today, Probabl is excited to introduce Skolar, a new, fully open-source educational initiative, built directly from your feedback and all the lessons we’ve learned along the way. Developed and extended by those same core developers of scikit-learn, Skolar is designed specifically for data science practitioners, offering hands-on, high-quality learning resources grounded in real-world applications and open-source values.

Skolar exists to boost our shared values: openness, teamwork, and practicality. It offers clear, interactive tutorials and structured courses carefully designed to match industry challenges and specialized use-cases. But even more importantly, it captures the true spirit of open source: encouraging collaboration, peer-to-peer learning, and guidance from experts.

Right now, we’re just at the beginning. Today, you can dive into our Scikit-learn Associate Practitioner online course, adapted from the popular Inria MOOC but enhanced with new material on unsupervised learning, especially clustering.

The next stages, professional and expert levels, will be released soon. We’ll also add more courses covering other open-source libraries such as skrub (for data wrangling), hazardous (for survival analysis), and fairlearn (for fairness). Additionally, our scikit-learn team is planning to create industry-specific modules tackling real-world needs in fields like healthcare, finance, medicine, and beyond.

At its core, Skolar is about empowering people through education, driven entirely by our passion for openness and collaboration. We firmly believe that true open data science begins with community-built learning resources. We warmly welcome you, whether you’re a contributor, learner, teacher, or just someone curious, to join us. Help shape Skolar’s future and support open-source education in data science.

Create your account on Skolar today: https://skolar.probabl.ai

Contribute to the scikit-learn course contents, or contribute to the learning platform’s backend or frontend.

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