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.
- CodeSeniorNew
PDE Solver for Subsurface Reservoir Flow
Implement MPFA-O discretization for pressure on a tetrahedral mesh with explicit fault transmissibility (Aavatsmark et al. 2002 formulation). Couple to a temperature equation vi…
- Numerical Pdes
- Finite Volume
- Newton Krylov
Scientific Computing and Numerical Methods - ResearchIntermediateNew
Kernel Methods vs. Deep Learning on a Tiny-Data Drug-Discovery Task
You receive (or download) 3 public ADMET datasets from MoleculeNet (e.g., BBBP, Lipophilicity, FreeSolv). For each, train both: (a) a Gaussian process with a Tanimoto kernel ove…
- Kernel Methods
- Gaussian Processes
- Neural Networks
Advanced Machine Learning - AnalysisIntermediateNew
Compare Kernel SVMs and Gradient Boosting on Imbalanced Tabular Data
You receive a 220,000-row anonymized loan-default dataset with mixed numeric and categorical features and a ~6% positive class. Train and evaluate (1) an RBF-kernel SVM with pro…
- Kernel Methods
- Gradient Boosting
- Model Selection
Machine Learning - CodeBeginnerNew
Stack Five Models for a Kaggle-Style Forecasting Bake-Off
You receive a pseudonymized dataset of 24 months of daily shipment volumes across about 200 origin-destination lanes plus weather and holiday features. Train 5 base models, use …
- Ensemble Methods
- Time Series Forecasting
- Feature Engineering
Advanced Machine Learning Practice your coursework on real scenarios.
Every challenge is shaped from real industry context — not generic exercises. The work mirrors what your degree prepares you for.
Why Ewance
- 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.