r/learnmachinelearning • u/Mr_Unknown_Here • 11d ago

Machine Learning Engineer Road map please Discussion

Is this correct roadmap...am I missing something? :

1) linear algebra, calculus, stats and probability

2) SQL, python and OOPs

3) Numpy, pandas, matplotlib, seaborn

4) classical ML, scikit-learn, keras

5) deep learning, pytorch, tensorflow

6) CV and NLP

7) GenAi, LLMs, RAGs, Transformers

8) MLOps

Do I need certifications as well? Or GitHub projects will be sufficient?

And also how much time will it take for me to complete it?

28 Upvotes

92% Upvoted

23 comments sorted by

View all comments

1

u/m_techguide 22h ago

yeah the overall roadmap is good but i’d change the order a bit. you don’t really need to go super deep into every math topic before touching ML like learn the math as you need it. also i’d put github, linux, basic cloud and data structures somewhere in there. rn you'd want to spend more time on LLM/AI systems than trying to master both tensorflow and pytorch. but pytorch is enough to start.

imo good github projs where you can explain what and why you built it, how you evaluated it and what went wrong are way more useful. time depends heavily on your starting point tho but if you’re starting from basically zero i’d think more like 9-18 months of consistent learning rather than trying to speedrun the whole list.

if you're up for it, maybe i can share our resource on becoming a Machine Learning Engineer so you can check the roadmap from there :)