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Image-Classification Model for a Quality-Control Line at a Bottling Plant

FreeVerified credential3 weeksIntermediate

Overview

What this challenge is about.

Train an image classifier for a bottling plant quality line using transfer learning, then earn a verifiable certificate.

The scenario

The Sevilla beverage company (around EUR 120M revenue, single bottling plant, 3 lines) cannot replace human inspectors entirely (regulatory + labor) but wants to triple inspection throughput on the highest-defect SKUs.

CredentialBlockchain-anchored
ShareableLinkedIn-ready
LanguageEnglish
PaceSelf-paced

The Brief

What you'll do, and what you'll demonstrate.

Build a deep-learning bottle-defect classifier that hits 95 percent recall on each defect class at under 200ms latency on Jetson Nano.

Earning criteria — what you'll demonstrate

  • Apply transfer learning to a constrained-hardware computer-vision task
  • Compare deep-learning vs feature-engineered baselines honestly
  • Evaluate models with cost-asymmetric metrics (false positives vs false negatives)
  • Recommend a deployment architecture respecting edge-hardware limits

Program Fit

Where this fits in your program.

Sharpens the same skills your degree expects you to demonstrate.

Machine Learning (CS Elective)

Master · General Studies

Strong alignment

This challenge maps to Machine Learning (CS Elective) at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.

One more thing

You can put a credential on your CV by Friday.