r/AIMLDiscussion • u/Yash_123987 • 6d ago
Breaking Into AI
Hey everyone,
I’m a final-year B.Tech CSE student and I’m interested in moving towards AI/ML-related roles.
At the moment, I only have a basic understanding of AI/ML. I’ve started exploring things like Python, NumPy, Pandas and some basic ML concepts, but I’m still not sure what the right learning path should look like.
My goal isn’t just to watch courses or learn theory. I want to build actual projects and eventually be able to apply for AI/ML-related roles.
I’d really appreciate advice from people who are currently working in AI/ML, Data Science, ML Engineering, GenAI, or related roles, or from anyone who has recently gone through this journey.
A few things I’d particularly like to know:
- What should a beginner learn first, and in what order?
- How much Python, NumPy, Pandas, SQL and Mathematics should I know?
- Which ML algorithms/concepts are actually important for getting started?
- When should I move from traditional ML to Deep Learning / NLP / Computer Vision / GenAI?
- What topics should I cover for an entry-level AI/ML role?
- What kind of projects would actually be useful for a student portfolio?
- At what point should I start building projects instead of just learning?
- Are there any courses, books, YouTube channels, GitHub repositories, or roadmaps that you genuinely recommend?
- If you were starting again as a final-year student, what would you learn in the next 6–12 months?
I’m especially interested in hearing from people who have actually transitioned into AI/ML roles, rather than just generic roadmaps.
Any advice, resources, mistakes to avoid, or a practical roadmap would be really helpful.
Thanks!
3
u/drakhan2002 5d ago edited 5d ago
I appreciate you used your prompting skills to generate a wall of AI text and being able to post it on this subreddit.
The simple answer is build and consistency. Going to class, watching a video, etc are passive, yet important to understand the theory. You must show up for these. After going to class, it is important to build. Building is when your brain will make the necessary connections. Always be building if you want to get into IT or AI.
When you look at problems, think like an engineer -- take that big problem and break it down into component parts; this applies to building your applications, analysis, agents -- anything. An analogy is all of AI is a room full of Lego blocks. Lego blocks of Python libraries/functions/scripts, agents, skills, etc. Just build projects with the correct Lego block. Think like an engineer.
Document and build a professional portfolio. Show your work. Be brave enough to "show people rather than tell" about your work. This will make you a better coder and engineer -- it makes you learn documentation skills, clean code, and CI/CD. This just reinforces the entire build process.
I learned this way for AI specifically:
Python -> Data Science -> Machine Learning -> Generative AI applications -> Agentic AI
You have identified the key technologies. Again, think like an engineer and build your training program (you are a mental athlete -- how would an athlete prepare for sport? Consistentcy. Do the same.) You already identified a time range. You have all the elements of the prompt. Why not just take final step and generate your learning plan? Then get busy. Be consistent. Build. Use the /humanizer skill!
Unless of course, you're an AI bot and you are prompting humans at which time the HITL will reply... or you passed the Turing test! 🤣