AI & Data
Computer Vision Challenges
Computer Vision challenges put you to work teaching machines to see. You'll develop skills in Image Processing and CNN Classification, build pipelines with OpenCV, tackle Object detection and Segmentation, and adapt pretrained models through Transfer learning.
From there you'll handle the harder edges — Custom architectures, 3D vision, Real-time inference, and Computer Graphics — building and deploying vision systems the way applied research teams actually do. Each challenge you solve earns a verified credential you can share with recruiters.
- CodeBeginnerNew
Calibrate a Multi-Camera Rig for Warehouse Robotics
You will design and prototype a calibration workflow using a printed ChArUco board (a chessboard with embedded ArUco markers). You receive a sample dataset of 200 raw frames per…
- Camera Calibration
- Multi View Geometry
- Opencv
3D Vision and Multi-View Geometry - ResearchIntermediateNew
Multi-Task Learning for a Healthtech Triage Model
You receive 40,000 anonymized de-identified intake-form records with two labels: urgency tier (4 classes) and routed sub-specialty (12 classes). Train (1) two independent classi…
- Multi Task Learning
- Transfer Learning
- Transformer
Open coursework - CodeIntermediateNew
Prototype a Computer-Vision QA Tool for a Robotics Manufacturer
As a 4-person team, build: (1) a labeling pipeline on around 2,000 component images (Label Studio is fine); (2) a transfer-learned classifier or a small segmentation model that …
- Computer Vision
- Transfer Learning
- Model Deployment
AI Software Engineering Group Project - AnalysisBeginnerNew
Sales Performance Analysis for a 40-Person SaaS Scale-Up
You will receive a dataset containing 500+ sales opportunities with fields like deal value, stage, source, close date, and account size. Your challenge is to design a data mart …
- Data Warehousing
- Etl
- Olap
Business Intelligence Develop in-demand professional skills.
Each challenge names the skills it strengthens. Over time, your profile fills with the competences a hiring manager would actually look for.
Why Ewance
- CodeIntermediateNew
Triage Medical-Imaging Annotations with a Small Vision Model
Train a binary normal/abnormal classifier on the public CheXpert or NIH ChestX-ray14 dataset. Use temperature scaling to calibrate the output, then define abstention thresholds …
- Image Classification
- Transfer Learning
- Calibration
Applied Machine Learning - ResearchSeniorNew
Plan a Parameter-Efficient Fine-Tuning Strategy for a Big-Tech AI Lab
You will produce (1) a 6-page survey of four PEFT methods (LoRA, adapters, prefix tuning, IA3) with their strengths, weaknesses, and parameter footprints, (2) a one-page decisio…
- Parameter Efficient Fine Tuning
- Transfer Learning
- Fine Tuning
Open coursework - ResearchFoundationalNew
Diagnose GreenBite's Plant-Based Chicken Launch Readiness
Work only from the four items in the provided materials: the GreenBite launch-readiness case file, the draft consumer survey instrument left by the previous analyst, the nationa…
- Survey Design
- Data Analysis
- Consumer Behavior
Open coursework - DesignIntermediateNew
Rescue a 3x3 Convolution Accelerator on Zynq UltraScale+
Working only from the materials provided, reach and defend an engineering judgment, then deliver the working accelerator that proves it. Read the engineering case file ('vision-…
- Systemverilog
- Fpga
- Axi Stream
Open coursework - Browse challenges
Explore role
Strategy Analyst
Frame the business question, model the options, build the recommendation. From market sizing to competitive analysis, this role is where strategy consulting meets in-house decision-making.
- 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…
- Self Supervised Learning
- Vision Transformers
- Pytorch
Advanced Deep Learning - CodeIntermediateNew
Few-Shot Defect Classifier for a Fast-Onboarding Industrial AI Vendor
You receive a multi-customer defect dataset (8 historical customers, 4-6 defect classes each). Treat 6 customers as the meta-training set and 2 as the held-out 'new customer' sc…
- Meta Learning
- Few Shot Learning
- Prototypical Networks
Meta-Learning, Transfer Learning, and Multi-Task Learning - 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) - CodeIntermediateNew
Multi-Sensor Late-Fusion Prototype for an Indoor AGV
Use the public KITTI dataset (or a similar paired LiDAR+RGB dataset) restricted to static-obstacle classes. Implement a late-fusion baseline: a LiDAR-only detector (PointPillars…
- Sensor Fusion
- 3d Object Detection
- Perception
Open coursework Get recognized by recruiters and employers.
Credentials are blockchain-anchored via LearnCoin — tamper-evident, portable, link-shareable on LinkedIn and beyond.
Why Ewance
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.