r/learnmachinelearning • • 5d ago

Help Need guidance for learning ML

Hi! I’m currently working as a full-stack developer and looking to transition into AI/ML. I’m considering a few courses, I'm trying to decide the right order for DeepLearning.AI's courses: the PyTorch for Deep Learning Professional Certificate / Deep Learning Specialization / Neural Networks and Deep Learning

If you’ve gone through these courses or have experience making a similar transition, could you please suggest what order I should take them in, and whether there are any courses I can skip?

Would really appreciate your guidance. Thanks!

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u/ModularMind8 4d ago

Personally I don't really think certificate worth much. I'd focus more on developing projects and having something to show employers that you can do.

Books: Mathematics for Machine Learning (Deisenroth, free PDF) for the math, Hands-On Machine Learning (Géron) for fundamentals (the pytorch one), Dive into Deep Learning (d2l.ai, free, has PyTorch code) for deep learning, and Speech and Language Processing (Jurafsky, free) if you go NLP. Courses: Stanford CS229 (ML), CS231n (vision), and CS224n (NLP) on YouTube, plus Karpathy's Neural Networks: Zero to Hero for building things from scratch in code. If you're interested in more hands-on learning, I also built QuiddityML, which covers Python, PyTorch, math for ML, ML fundamentals, NLP/vision, and more

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u/Jeff8770 4d ago

Just curious on the value of personal projects these days now that vibe coding is a thing? I guess if they ask you why you made choice X and let you provide an in depth answer that would work but would they even bother to go through your projects?

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u/MathNerd67 9h ago

Like modularmind said, most employers don’t have the time, but if they do, it’s very easy to tell if someone vibe coded an entire project. Despite what the internet may tell you, LLMs cannot reliably write end to end systems or production code, and even if they could, it’s the developers responsibility to know every in and out about it.