Transfer Learning
If you like applying Transfer Learning, every challenge here gives you a chance to practice it on a real industry brief.
- CodeIntermediateNew
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) - CodeIntermediateNew
Semantic Segmentation for a Solar-Panel Inspection Drone
Use a publicly-available solar-panel dataset (or the PV-Defect-Detection dataset). Fine-tune a small U-Net or SegFormer-tiny on panel/no-panel pixel-level segmentation. Evaluate…
- Semantic Segmentation
- Cnn
- Transfer Learning
Computer Vision (Undergraduate) - CodeIntermediateNew
Build a Crop-Disease Classifier for a Smallholder Agritech Startup
You receive a curated 22,000-image cassava-disease dataset across 5 classes (4 diseases + healthy) plus a labeled 1,200-image held-out test set. Train a CNN classifier (start wi…
- Image Classification
- Cnn Architectures
- Transfer Learning
Deep Learning for Computer Vision - ResearchIntermediateNew
Drug-Repurposing Candidate Screen with Embedding Similarity
You receive (1) a list of 15 known therapeutic candidates (SMILES + ChEMBL identifiers) for a single rare disease, (2) a database of about 4,500 marketed drugs (SMILES + ATC cod…
- Molecular Embeddings
- Similarity Search
- Transfer Learning
Machine Learning for Healthcare and Biomedicine 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
- CodeIntermediateNew
Team Practicum: Build a Crop-Disease Classifier with a Field Partner
You receive a labeled dataset of about 8,000 phone photos plus around 1,200 unlabeled photos from a held-out county. Audit and clean the labels (expect 5-10% noise), train a Mob…
- Transfer Learning
- Pytorch
- Model Evaluation
AI/ML Practicum and Hands-on Lab
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.