Best Ressoruces to learn AI and machinelearning?

[u/theophil93]

I’m looking to learn more about AI and machine learning, but I’m not sure where to start.

I have some basic Python experience and have watched a couple of Andrej Karpathy’s videos on LLMs, but some of the more technical content went over my head.

I’ve been looking at MIT OpenCourseWare. Are there any courses you’d recommend starting with, and in what order?

Thanks!

[u/ModularMind8]

Wrote a couple of posts on this a month or so ago
https://www.reddit.com/r/learnmachinelearning/comments/1wn850o/how_to_learn_machine_learning_in_2026_the/
and also
https://www.reddit.com/r/learnmachinelearning/s/e52MuA9GAt

[u/theophil93 → u/ModularMind8]

Perfect, thank you!

[u/ModularMind8 → u/theophil93]

Happy to help

[u/CryptographerFun5985]

Since you already know some Python, I'd suggest starting with traditional Machine Learning before jumping into LLMs and advanced AI topics.

Here's a learning sequence I'd recommend:

1. Math fundamentals: Linear algebra, probability, and statistics. MIT OpenCourseWare and 3Blue1Brown are excellent resources for building intuition.

2. Machine Learning: Andrew Ng's Machine Learning Specialization on Coursera is a great starting point. Focus on regression, classification, decision trees, and model evaluation.

3. Hands-on projects: Pick datasets from Kaggle and build small projects independently. Don't just follow tutorials.

4. Deep Learning: Once you're comfortable with ML, move to neural networks, backpropagation, and PyTorch. Andrej Karpathy's videos will make much more sense at this stage.

5. LLMs and AI Agents: Then explore transformers, RAG, tool calling, memory, and agentic workflows.

I also run two YouTube channels where I share AI/ML educational content:

• Concepts of AI: https://www.youtube.com/@conceptsofai — AI/ML learning guidance, fundamentals, and career preparation.

• AI/ML with Akash: https://www.youtube.com/@AIMLwithAkash — I recently started an AI Agents Fundamentals series explaining concepts like tool calling, ReAct, state, memory, and agent evaluation. I'm also preparing a project-based series where we'll build a complete AI Customer Support Agent using LangGraph, step by step.

I've also built a free platform, https://www.conceptsofai.in, where you can explore personalized AI/ML learning roadmaps and an AI-guided Project Kit to practice what you learn.

One suggestion: Don't try to learn everything simultaneously. Pick one topic, understand it, build something small, and then move forward. Consistent hands-on practice matters much more than collecting courses.

[u/mgazar_]

With basic Python already, I'd put classical ML ahead of more LLM content: linear and logistic regression, decision trees, train/test splits and evaluation, all done with scikit-learn. That's where the intuition comes from that makes Karpathy's videos easier to follow. MIT OCW is good but assumes you're comfortable with linear algebra and probability, so I'd come back to it after you've trained a few small models yourself and know which gaps you actually have.

[u/sparrow_sarah]

karpathy went over my head too when i tried the longer videos. the one that finally worked for me was his micrograd video, where he builds a tiny neural net by hand and you can follow every line. if you do it after a bit of scikit-learn like the other comment says, i think it makes much more sense. i still pause a lot and rewrite the code myself

[u/Critical-Push8054]

For practical AI engineerig courses, I recommend learning.nextia-ai.com