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.
- CodeBeginnerNew
Image-Classification Model for a Quality-Control Line at a Bottling Plant
Train an image classifier on 8,000 labeled bottle images (3 defect classes + 'ok'). Use transfer learning from a pre-trained backbone (EfficientNet-B0 or MobileNetV3) — the line…
- Deep Learning
- Supervised Learning
- Ml Applications
Machine Learning (CS Elective) - ResearchSeniorNew
Pretrain a Small Vision Transformer with Self-Supervised Learning
You receive 80,000 unlabeled 224x224 histology tiles plus 4,000 labeled tiles split into train/val/test. Pretrain a ViT-Small using a self-supervised method of your choice (DINO…
- Supervised Learning
- Vision Transformers
- Pytorch Or Tensorflow
Advanced Deep Learning - AnalysisIntermediateNew
Transfer-Learning Backbone Bake-Off for Retail Product Tagging
You receive 80,000 retail product images tagged with multiple labels from a 250-tag taxonomy. Use each of the three pretrained backbones via two transfer strategies: (1) linear …
- Transfer Learning
- Fine Tuning
- Supervised Learning
Meta-Learning, Transfer Learning, and Multi-Task Learning - CodeIntermediateNew
Forecasting Model for Online-Game Daily Active Users
Build forecasts at 14-day horizon per region using: (1) classical baseline — SARIMA or Prophet; (2) ML approach — gradient-boosted regressor on engineered features (day-of-week,…
- Supervised Learning
- Time Series Forecasting
- Python Or Javascript
Machine Learning (CS Elective) 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
- CodeBeginnerNew
Predict Subscription Churn for an EdTech Platform
You receive a CSV with about 18,000 student-month rows: features include login frequency, session length, quiz scores, parent app opens, and plan tier. The target is whether the…
- Supervised Learning
- Logistic Regression
- Gradient Boosting
Machine Learning (Undergraduate) - ResearchSeniorNew
Self-Supervised Pretraining for a Pathology Foundation Vendor
You receive a public pathology dataset (about 80,000 unlabeled whole-slide-image patches plus a labeled 8,000-patch subtype-classification subset across 4 classes). Pretrain a R…
- Supervised Learning
- Medical Imaging
- Transfer Learning
Machine Learning for Imaging and Medical Image Analysis - CodeBeginnerNew
Churn-Prediction Model for a B2B Vertical SaaS
Use 18 months of anonymized data (provided) covering: usage events, login frequency, support tickets, NPS responses, billing health, feature adoption, practice firmographics. De…
- Supervised Learning
- Python Or Javascript
- Ml Applications
Machine Learning (CS Elective)
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.