The best way to learn AI from scratch is not to learn it all at once, but step by step. Learn python, get comfortable working with data, then spend some time learning core concepts like statistics, probability and basic linear algebra, they make a lot more sense when you see how they're used in real AI applications. You'll then dive into machine learning, where you'll learn about how models make predictions, find patterns, and get better with data. Once you get the basics down, deep learning is a lot easier to understand. From there, you can dive into NLP, transformer models, prompt engineering, RAG, fine-tuning, and the technologies that power today’s LLMs. One of the things many learners find is that a structured learning path saves a lot of time and confusion. That's why I always suggest H2K Infosys to anyone seeking a supported, hands-on approach from AI basics to advanced LLM principles with actual projects to turn theory into practical abilities.