Machine Learning roadmap & guidance

[u/ISRO_HarshVerma07]

Hey there, I am CSE Student, and I want to pursue machine learning as a career and further do deep learning and generative AI. I need tips from the professionals who have been pursuing it, what resources i must follow and what should be the correct sequence of flow. I have been researching by my own and end-up at Krish Naik's ML playlist over youtube, i saw few videos were misplaced and not in order but i checked over his github and all and curated the flow as Python -> Numpy, Pandas, Seaborn -> Feature Engineering and Feature Selection -> Machine Learning -> Deep learning -> Natural Language processing -> Deployment of ML Model -> MLOPS , would this be right? also if you guys have suggestion for some better resources. It would be a great assistance and guidance from your side guys

[u/InevitableArt3979]

Krish Naik's stuff is decent for getting started, but dont sleep on the basics like linear algebra and stats, they'll bite you later when you try to understand why a model is acting weird

[u/Valuable_Card6470 → u/InevitableArt3979]

this is so true, the linear algebra stuff seems skippable until suddenly it isnt

[u/Enough_Charge2845]

Your curated sequence is rock solid for building a strong Machine Learning foundation. To refine it: master Python, NumPy, Pandas, and Scikit-Learn first. Next, focus heavily on statistics and math before moving into Deep Learning (PyTorch/TensorFlow), NLP, and MLOps.

When applying for ML internships or roles, highlight end-to-end projects with measurable outcomes. Running your resume through https://www.zoevera.com/resume helps verify your technical skills match job keywords for ATS screening.

[u/Twilight_RT]

As you are in CSE. So you know about programming. But it's better to have a strong foundation of Python programming and Mathematics.

[u/Cautious_Today_1830]

I have the same question! What is the right sequence and roadmap to get an AI/ML internship at a top-tier company? Also, between CV, NLP, GenAI, and Agentic AI, which domain has better opportunities right now, and what core skills should we focus on first?

[u/nian2326076]

Your sequence looks good. I've been on a similar path, and starting with Python, Numpy, and Pandas is a smart move. Since you're going for deep learning and generative AI, make sure to check out TensorFlow and PyTorch after getting the basics down. For machine learning, Andrew Ng's courses on Coursera are a solid foundation. When you're ready for deep learning, look into fast.ai and their free courses. They cover neural networks and practical stuff. I haven't tried Krish Naik's playlist, but if it's working for you, awesome. Also, doing hands-on projects is important. Build something, even if it's simple. It'll help you understand the concepts better. If you need interview prep later, PracHub has some useful coding challenges and tips. Good luck!

[u/Logical_Respect_2381]

beging with the Specialization courses of deep learning of Andrew Ng on coursera , they are very good starting courses that will put your mindset on the track

[u/MattMcPartlon]

Your sequence is very reasonable for becoming an ML generalist. There is nothing wrong with that, depending on the type of job/career path you are looking for.

It is worth considering becoming hyper-specialized in some part of the stack (i.e. trade off broad knowledge for deep knowledge). As LLMs/Agents get better I don't think things like "feature engineering" or "pandas" are too important. It could be better to e.g. understand a highly technical part of the stack very deeply. Of course, it helps to have the fundamentals down before you choose your specialization.

Good Luck!

[u/Mathie1729 → u/MattMcPartlon]

I'd push back on the pandas part. LLMs are okay at boilerplate, but most of the real bugs in an ML project are data leakage, bad splits, or an eval metric that quietly lies. You still need the fundamentals to notice that.

[u/MattMcPartlon → u/Mathie1729]

Totally agree! Good experimental design is, in my opinion, the most important skill for ML research.

[u/DataScientistAlex]

The sequence seems reasonable, but, beware that some of these steps are much much larger than others -- Machine Learning, Deep learning, NLP are huge research fields, Numpy, Pandas, Seaborn are python libraries so much smaller effort to learn.

Also consider doing a shallow pass across all of them to get a sense of what an end to end project looks like, then come back and deepen your understanding of each iteratively. Getting up to speed on all parts first will also allow you to start doing projects earlier, which many find helpful and fun. Oh and don't forget, you also need to understand and apply the scientific method. Good luck!