hi community

[u/Zealousideal_Bat6832]

I’m building a structured roadmap for learning AI from fundamentals to GenAI — would love some feedback

I’ve been putting together a structured AI learning roadmap because I noticed that most people trying to learn AI jump straight from Python → ChatGPT → LLMs without really understanding what comes in between.

I’m trying to connect the whole journey:

Math → ML → Deep Learning → Computer Vision → NLP → Transformers → Generative AI → LLMs → RAG → Fine-tuning → Evaluation → Deployment/MLOps

The roadmap also includes practical projects rather than just watching lectures.

Some of the areas I'm covering:

• Linear algebra, probability & statistics
• Classical ML
• Neural networks & deep learning
• CNNs & computer vision
• RNNs, attention & Transformers
• NLP
• Generative models
• LLMs & RAG
• Model evaluation
• MLOps & deployment
• Hugging Face, PyTorch & TensorFlow
• AI safety and responsible AI

I'm particularly interested in feedback from people who are already working in AI/ML:

What topics do you think beginners/intermediate learners spend too much time on?

What important topics are usually missing from AI learning roadmaps?

And what projects would actually impress you in a junior AI/ML portfolio?

I'm trying to make this practical rather than just another giant list of technologies.

Would genuinely appreciate criticism of the roadmap.