Validate your ML expertise with the only official scikit-learn certification.
Official, proctored, hands-on certifications designed by the people who maintain scikit-learn. Train with Skolar. Get assessed on real ML work. Earn a verifiable credential.
What is your scikit-learn level?
120 minutes, coding plus theory, documentation allowed, online or at test centers, verifiable badge.
Associate Practitioner
Best for: Practitioners with foundational scikit-learn experience and early-career data scientists.
You are able to:
- Build end-to-end pipelines
- Apply correct preprocessing and evaluation
- Avoid common leakage and validation mistakes
Professional Practitioner
Best for: Practicing data scientists working on real ML projects.
You are able to:
- Design robust pipelines under real constraints
- Tune models and evaluate them correctly
- Apply best practices consistently across projects
Expert Practitioner
Best for: Senior practitioners and ML experts.
You are able to:
- Debug and reason about complex ML workflows
- Write and use advanced scikit-learn components
- Make informed design trade-offs in production contexts
Train with the same standards you will be evaluated on.
Skolar provides structured learning paths and hands-on exercises aligned with the certification expectations. Practice realistic ML workflows. Strengthen weak spots before the exam.
Things candidates ask before they sit.
Training
Where can I find training to prepare for the certification?
Is Skolar free to use, or do I need a subscription?
Will completing the Skolar track guarantee I pass the exam?
Is there a certificate of completion for each course on Skolar?
Certification
Do I have to pass all 3 levels successively?
What is the exam format?
Can I reschedule my session?
Which OS can I use?
For bulk packages, is this the same certification individuals take?
How many attempts do I have?
The credential the maintainers sign.
Three levels. One library. Real ML work, scored against the standards the people who ship scikit-learn use every day.