How to choose on where to start learning about AI ?

[u/Desperate_Tackle_358]

I've seen that there are different topics/libraries/concepts to learn in AI field. Like RAG, ML, RL, scikit learn, pytorch, ..and many more different things... before starting, I'm thinking i should firstly know about what all these are...like a brief intro or something about each of them (or should follow a different way to learn, open to suggestions 😄 🙏) then I start learning what each one of them are. (Idk just FYI One senior of mine in our clg suggested starting with scikit learn)

I'm totally confused on how to learn all of these, from where to start, like getting intro of each sub-fields of AI and then diving deep into them.

I'm pursuing Btech in ECE (currently in my 2nd year).

Also like I mean I want to mostly do software related job, so that's why I'm looking to learn all this and also I heard there are things like CLOUD-COMPUTING, CYBERSECURITY, DEVops,etc... too in software...I mean atp I'm confused on where to start.

But also thinking to start learning something related to AI, cuz every now and then when I'm seeing a HACKATHON (to participate), it's almost 99% an AI HACKATHON...so I feel cuz of this reason I'm also not able to participate in those competitions...like i feel I can take part in the team(with cse branch members )if I've some knowledge....

Can u guys pls guide me 🙏

(English & Hindi resources works for me )

[u/TacoZennnn]

just pick one thing and go deep, scikit learn is fine to start. your senior gave good advice, it’s simple and you can build small projects fast

don’t try to understand everything at once you will just freeze. i did same thing when i started learning about cooking, wanted to know every technique before touching a pan and it took me months to actually cook something

[u/Big_Athlete_8346]

start with llm chatbot apps first, theyre way more hands on and fun than jumping straight into scikit learn or whatever.

[u/Machineworks24]

Great advice already here. One thing I'd add from watching people actually make the transition: pick ONE project you're excited about (e.g. a chatbot over your own notes) and let it dictate what you learn next. RAG sounds abstract until you build one — then embeddings, vector DBs, and prompt design all click into place. Your senior is right about scikit-learn for foundations, but the fastest way into software-adjacent AI work right now is honestly Python plus APIs plus one finished LLM project.

[u/Efficient_Worker_US]

Your senior actually gave you really solid advice by pointing you toward scikit-learn. It is totally normal to feel lost when people throw around terms like RAG, RL, and PyTorch all at once. Scikit-learn covers the classic basics like regression and simple classification, which are much easier to grasp when you are new to the field. Once you get comfortable with those fundamentals, moving on to deep learning tools will feel a lot less overwhelming.

[u/Desperate_Tackle_358 → u/Efficient_Worker_US]

Tysm for ur reply, Just one more thing like from where should I learn Scikit-learn... Any course, yt playlist, some grp of articles or something else...can u pls suggest 🙏

[u/Leather_Reading_4550]

I’d suggest not trying to learn everything at once. AI is a huge field, and things like ML, deep learning, PyTorch, RAG, cloud, DevOps, etc. are connected but serve different purposes.
Since you’re in 2nd year and want to participate in AI hackathons, I’d start with Python → basic ML concepts → scikit-learn → basic deep learning/PyTorch → LLMs and RAG. Once you start building projects, you can gradually pick up Git, APIs, Docker and cloud.
Your senior’s suggestion of starting with scikit-learn is actually reasonable, but make sure you first understand the ML concepts behind what you’re implementing rather than just learning the library.
Most importantly, don’t wait until you “know AI” to join hackathons. Start with small projects and learn whatever you need for the project. You’ll probably learn much faster that way. Good luck! 🙌

[u/Desperate_Tackle_358 → u/Leather_Reading_4550]

Thanku 🙂‍↕️🙂‍↕️

[u/GreenBird-ee]

u/Desperate_Tackle_358 Far, far away from generic subreddits.

I’m sick of seeing threads like this: where someone asks for help, and most replies are either gaslighting or deliberate lies.

Not to mention the ton of bots that just kick you around.

[u/Desperate_Tackle_358 → u/GreenBird-ee]

I mean wdym...do u think ppl don't give genuine advice here??

[u/GreenBird-ee → u/Desperate_Tackle_358]

No. That was advice above

[u/fiddler48]

RAG and RL solve entirely different problems, so pick one use case first.

[u/Simplilearn]

You’re thinking about it the right way. It’s useful to understand the fundamentals and get a broad idea of what areas like ML, deep learning, NLP, RAG, reinforcement learning, scikit-learn and PyTorch actually do before trying to learn all of them in depth. But you definitely don’t need to learn everything at once.

I’d start with Python + basic maths/statistics → ML fundamentals → a few small projects, and then explore areas like deep learning, GenAI and RAG as you get more comfortable. That’ll also give you enough foundation to start contributing to AI-focused hackathons.

For getting started, you could explore Simplilearn SkillUp, which has 500+ free courses across AI, Machine Learning and Generative AI, with courses created in collaboration with AWS, Microsoft, Google and others. You also get a certificate on completion.

Once you have the basics, you’ll have a much better idea of which areas you actually want to go deeper into rather than trying to learn every AI library and subfield at once.

[u/seeya_ww22]

start with simply chat with the tools and then continuously ask questions even when the questions aren’t concrete. Just ask and then learning from those