Machine Vision and Quality Inspection

No. The specification modules teach the arithmetic you will use to buy one. The bench test uses a phone and a lamp. The detector trains on the track's labelled dataset in Colab.

The Computer Vision with OpenCV course, for the classical tools and the measurement habits. The Deep Learning course helps for the YOLO modules, but the notes are self-contained.

There are two projects. In the first you train, evaluate, threshold-sweep and export a YOLO defect detector on 640 labelled surface images. It ends with the recall-per-defect sentence QA would sign. In the second you specify a complete inspection station for a real product: camera, lens, lighting, trigger and timing. You bench-test the lighting choice on your own photos.

Yes. Every module, practice task, project and assessment. You create an account so your progress is saved and the assessments can be marked. The certificate is the only paid item, and only if you want it.

An EDWartens Certificate of Completion, issued when you have finished the modules and passed the final assessment (the project is optional practice), with a verification code anyone can check. It is not a vendor credential and is never described as one.

The creators named in the Credits block at the foot of this page, on their own YouTube channels. EDWartens did not make the videos and the creators are not affiliated with EDWartens. What EDWartens wrote is the study plan, the notes, the practice tasks, the projects and the assessments.

Plan on about 14 hours in all, including the video lessons, notes and practice tasks. It is self-paced, so you work through the twelve modules at your own speed.

No. The lab is optional practice. You can skip it whenever you like, and it never blocks your progress, the final assessment or your certificate.

Yes. Every lab level for this course opens in your browser, free, with nothing to install.

The Machine Vision and Quality Inspection course has 12 modules. It opens with "The anatomy of an inspection station", goes through "Cameras and sensors", "Triggering, timing and the PLC", "Datasets and labelling" and "The inspection station project" and ends with "Final assessment". The full list with minutes is under "What you learn" on this page.

After the Machine Vision and Quality Inspection course: Turn feature size, field of view and belt speed into pixels, exposure and a lens focal length; Choose backlight, dark-field, dome or structured light by measured contrast, not opinion; Build the timing budget to the rejector and the PLC handshake with a heartbeat. These are the practical outcomes the course is written around, and the final assessment of 15 questions checks them.

For the Machine Vision and Quality Inspection course, software: Google Colab with a GPU runtime and Ultralytics YOLO; a phone camera for the bench test. Hardware: A phone, a torch, a lamp and white paper for the lighting bench test. No industrial camera required. The course itself is free.

Machine Vision and Quality Inspection is rated intermediate. Intermediate. Needs the Computer Vision with OpenCV course; the Deep Learning course helps for modules 7–10. It is written for engineers, developers and students applying AI to real work, and takes about 13.5 hours at your own pace.

Skills from the Machine Vision and Quality Inspection course (for example: turn feature size, field of view and belt speed into pixels, exposure and a lens focal length) are used in roles such as AI engineer, machine learning engineer, data analyst and automation developer, and in machine learning engineer work. The course gives you practical evidence of the skill; it does not promise a job, and employers decide on experience and interview.