AI & Data
Machine Learning Fundamentals Challenges
Machine Learning Fundamentals challenges put you inside the work of turning raw data into models that hold up. You'll develop skills in Supervised Learning, Classification & Regression, and Feature engineering, then validate your work with Train/Test Split and Cross-validation in scikit-learn.
From there you'll handle the harder edges — Hyperparameter tuning, Tree ensembles, Model Calibration, and Model selection under business constraints — moving toward Production model tuning and Feature pipelines the way working ML teams do. Each challenge you solve earns a verified credential you can share with recruiters.
- AnalysisBeginnerNew
Explain a Credit-Risk Model with SHAP for a Fintech
You receive a trained XGBoost credit-risk model (binary default prediction), the training feature schema (38 features), and a held-out 10,000-sample test set with labels. Comput…
- Shap
- Interpretability
- Fairness Analysis
Explainable and Interpretable AI - ResearchIntermediateNew
Compare Kernel Methods to Trees on a Genomics Classification Task
You receive a curated benchmark of about 12,000 labeled variants with ~120 numerical + ~40 string features. Fit kernel SVMs (RBF, polynomial, string), random forest, and XGBoost…
- Kernel Methods
- Svm
- Tree Ensembles
Statistical Machine Learning
How it works
From brief to credential, in six steps.
Step 01
Browse challenges aligned to your studies.
Step 02
Accept the one that fits your goals.
Step 03
Work through it with AI Copilot guidance.
Step 04
Submit for structured evaluation.
Step 05
Earn a verified credential.
Step 06
Add it to LinkedIn with one click.
Industry teams behind a decade of practitioner briefs
Hiring from this pool?
Sponsor a challenge and meet candidates through actual work.
Industry teams can shape briefs around the skills they hire for, then evaluate students on rubric-scored deliverables — not resumes.