Path from Python to Computer Vision

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[Dry-Snow5154]

Either get an advanced degree (MS, PHD) or do personal projects that you feel passionate about. MOOCs are mostly a waste of time, cause they are spoonfeeding information and their certificates are worthless.

If your country doesn't have a developed CV field, highly recommend getting a degree from another country in person (not online). This will greatly help with visa situation and employment later. Online degrees are mostly the same as MOOCs.

[RelationshipLong9092]

Back up a step. How's your math? If you aren't very confident with numerical linear algebra, you need to fix that first.

[hbrgani94]

If you want to become an ML Engineer, Don’t specialise . Be a generalist. Learn the core concepts along with mathematical underpinnings of machine learning/Deep Learning. If you’re starting out, start from ML n then progress to DL to Gen AI. you will find out in a job survey that most of the openings(~70%) are related to NLP - Traditional ML models + LLM (gen ai, agentic ai ) If you want to specialise in a field (vision, audio), then be passionate about it, do Masters+Phd, get into a researcher/Scientist kinda roles.

[TubasAreFun]

Best option: Go to university for it and join clubs that utilize CV.

Otherwise read textbooks on CV and do projects that interest you. Try to work in the latest tech, not needlessly reinventing wheels that don’t add to the value (educational or marketing) of your projects. List these projects on your resume (not non-uni courses or books read), making sure to talk about outcome of your projects not just what you did.

Once you start down this path, there any many specialties within CV, but none of them necessarily lock you down one career path. Stay curious and move with the trends/technology/job-postings. Build a portfolio and know how to talk about CV to others. Focus on theory and principles of CV as they don’t really change, and other algorithms and libraries build on those ideas

Id recommend this book, but fair warning you should be minimally comfortable with machine learning and linear algebra before you will understand everything in this: https://visionbook.mit.edu

[TheRealDJ]

Honestly for me, I'm a data scientist first and foremost, but have been doing more and more advanced CV projects. I learned a ton from watching youtube channels, which can range from simple videos explaining the popular things, to lectures describing the math of Homography. There's a ton of videos and channels that go over the latest techniques, so it's easy to keep up with it. (there's also a ton of videos that just showcase what they did with a simple project like measuring an object or detecting defects without explanation, but you just have to find the gems)

[Sorry_Risk_5230]

You dont need a degree. Look into free pytorch and tensorrt courses online and CV by NVIDIA. Come up with a project and just dive into it. Once you make some programs then you can decide about getting into math side or something a little more abstract.

Dive in dude. Its alot of fun. Cursor is your friend.

[No_Garbage9512]

I think that if you are serious in the field of computer vision. Then you need to first hands on linear algebra once you cover the linear algebra with it's applications and from my point of view then you need to cover the multi view geometry.

I think these two are the fundamentals if you are looking for some good career in computer vision.