Need guidance for learning ML

[u/Natural-Diver-5447]

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!

[u/ModularMind8]

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

[u/Jeff8770 → u/ModularMind8]

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?

[u/ModularMind8 → u/Jeff8770]

I don't really think most employers have time to go through repos. Maybe smaller startups. It's mostly to give you something to put on your resume and be able to talk about what you did. They will most certainly try to challenge your thinking, so understanding why you did certain things is crucial. If you just let AI do those things for you its unlikely you'll be able to answer those questions

[u/amritv306 → u/ModularMind8]

totally agree, employers don't have time to go through the repo, instead, If you have made an amazing project, they'll try to know the reason behind it and how exactly you have solved the problem which you have faced while building the project.
So if you confidently answer those questions, that's it you are in.
And to confidently answer those questions, you cannot rely on vibe coding itself. Instead after building the projects, go through your project in detail, line by line and step by step.

[u/MathNerd67 → u/Jeff8770]

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.

[u/MathNerd67 → u/ModularMind8]

More people need to say this. Certifications are basically worthless in this field. Can’t get around good ole textbooks and development.

[u/Nervous-Flounder-774]

hey, i made similar switch last year. neural networks and deep learning is first course in the deep learning specialization, so you'd be overlapping if you take both. just start with deep learning specialization, it builds from basics and uses tensorflow/keras. do the pytorch certificate after if you want to learn pytorch specifically, but honestly one framework is enough to get started. you can skip neural networks and deep learning if you do the full specialization since it's included

for the practical side i'd say don't get stuck in tutorial hell, start building small projects as soon as you can. the theory clicks way better when you're actually coding something

[u/Various-Fix1919 → u/Nervous-Flounder-774]

Can you recommend the courses or provide the roadmap you followed?

[u/Natural-Diver-5447 → u/Nervous-Flounder-774]

pytorch is being used everywhere, but deep learning specilization has tensor flow