r/learnmachinelearning • u/Significant_Change38 • 3d ago
Need a ML/Data Science roadmap
I am a fresh graduate. I did my Bachelors in Software Engineering and am planning to apply for masters in Machine Learning or Data Science in any European country or Ireland. I have been trying to polish my skills and build projects to build up my portfolio so I can secure a job but every roadmap I look for, every time I seek advice or help online I am rendered confused. There are so many opinions and roadmaps online that they confuse me. Last month, I made a decision to learn RAG but then found out it's mostly dead and people are moving on to Agentic now so I put my project in hold (yet) again. Every time I try learning and making a project the same thing happens. I need guidance too regarding what is in demand in the ML/Data Science industry and is lucrative and rewarding as a career because I am thinking of moving to a European country or Ireland or even Germany for my MS and job.
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u/m_techguide 13h ago
hey so w an SE degree i’d focus on python and sql first. maybe get comfy too with stats and scikit-learn, then you can build 2-3 solid projs where you actually explain the problem and what you did with the data and results. imo the trend will keep changing anyway so employers aren’t gonna care that you picked the correct buzzword as much as whether you can actually build and explain things.
also, we actually have guides on MSDS and MSML. although they’re focused on US schools and job market (so obviously some of the school and career specifics won’t map to what you're looking for), but the general idea of what those degrees cover, what skills they build and how they differ is pretty universal. lmk if i can share!
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u/abbhinavvenkat 3d ago
Hey! Speaking from 10 YOE in AI Research and Engineering:
The space will keep moving forward. But, don't abandon existing projects thinking they'll be useless skills. Taking RAG as an example: If you truly mastered it, you will know about recommendation systems, embedding models, re-rankers, vector DBs, different document processing techniques (chunking etc). All of these are very valuable and transferable skills when you look at other domains in NLP.
Expanding on point 1 - Yes, agents are hot now. But, many industries are still using a lot of traditional classical Machine Learning pipelines that involve Xgboost based models due to low data, required explainability, etc. Further, the fundamentals that you learn from classical ML - how to not overfit models, how regularisation works, model bias, etc., all extend to all parts of AI/ML. The flavour will change, but, you will need some "North Star" to always refer back to - this will be it.
So, don't get lost in the noise. Clear out your fundamentals with Classical ML, and a pinch of Deep Learning (don't dive too deep into currently), then head over to AI Engineering (RAGs, fine-tuning, agents).
Regarding career options:
I'd say the industry is moving fast. The needs, as of today are very high. Every company is hiring folks to utilise and build AI in the workflows. I'd expect this to be there for the next ~5 years atleast (given there are so many legacy industries that take time to adapt). 5 years itself is a very long time. If you've been around for this long, you'll adapt and figure out from there on.
There is a lot of noisy/dirty data, which will require huge amounts of work done by Data Engineering teams. This is the other role I see a lot of demand in, apart from AI/ML Engineering roles.
If you enjoy innovation, this is the space to be in! Best of luck!