Are you ready to be a Computer Vision Engineer?Free Computer Vision skill check.
The Computer Vision skill check is five multiple-choice questions, one each on Generalisation, Evaluation, Metrics, Optimisation and Regularisation. It is free, takes about three minutes, and grades you from Novice to Expert with every answer explained.
- 5 questions
- About 3 minutes
- Free
Choose a course
Every course in the AI Tutor has its own check: five questions, one per topic.
Career tracks 11 courses
For a new role: engineer, scientist, analyst or builder.
Sign up free to start. Your answers are graded when you finish and saved to your record.
The Computer Vision skill check.Five topics, one question each.
Question 1 is below exactly as the check asks it. The answer, and why it is right, come with your result.
Computer vision powers everything from autonomous vehicles to medical imaging to augmented reality. CV engineers need strong foundations in machine learning theory before specialising in image processing, object detection, and neural network architectures.
This diagnostic tests five ML fundamentals that underpin all computer vision work: model generalisation and overfitting, proper evaluation methodology, metric selection for imbalanced datasets (common in detection tasks), gradient-based optimisation, and regularisation.
Three minutes to discover your starting point — then decide whether to dive into the full CV learning path.
- Generalisation
Overfitting is especially problematic in CV where models memorise texture patterns.
- Evaluation
Proper train/test splitting prevents inflated accuracy claims in image classification.
- Metrics
Object detection tasks are inherently imbalanced: raw accuracy is almost always misleading.
- Optimisation
Understanding gradient flow is critical for training deep convolutional networks.
- Regularisation
Dropout and weight decay are standard tools for training vision models.
A model scores 99% on training data and 70% on validation data. What is most likely happening?
- AUnderfitting
- BOverfitting
- CThe validation set is too large
- DThe model is perfect
“Learn AI” means three things.This check tests one of them.
It tests the machine learning foundations every vision model rests on: generalisation, evaluation, metrics, optimisation and regularisation.
- 01This check
Machine learning foundations
How models learn from data: the statistics, training and evaluation underneath them.
For you ifyou want to train and judge models, as an ML engineer or data scientist.
- 02
Applied LLM engineering
Building software on models like Claude and GPT: prompts, retrieval, agents, evals and shipping.
For you ifyou write code and want to build AI features, not train models from scratch.
Take the Generative AI check - 03
AI for your job
Using AI tools well in the work you already do: briefing them, checking what comes back, keeping data safe.
For you ifyou do not plan to code and want AI to make the work you do faster and better.
Take the AI for Operations check
Three steps.About three minutes.
It is free and nothing is booked. Save the report to keep it on your account.
- 01
Pick a course
Any of the 21 courses in the AI Tutor, from Generative AI to AI for Teachers. Each check covers five topics.
- 02
Answer five questions
One question per topic, four options each. Nothing is marked until you finish.
- 03
See your readiness
Your readiness out of 10, a skill map, every answer explained, and the path through the course.
Programmes that build on this check.Your result shows where to start.
Each one teaches Computer Vision by building, and ends in deployed work graded against a rubric you can read.
Before you start.Three quick answers.
Why does this test ML basics instead of CV-specific topics?
CV is built on ML foundations. The full course covers CNNs, object detection, segmentation, and more — but you need these basics first.
Do I need to know PyTorch or TensorFlow?
Not for this quick check. The full assessment includes framework-specific coding challenges.
Is this relevant for robotics roles?
Yes — these ML fundamentals are prerequisites for perception systems in robotics.
Rather talk to a person?
Tell us what you want to do with AI. We’ll suggest a programme on a 15-minute call.