A simple, steady approach works best -
Start with the basics
First, get a rough idea of what AI actually is. People often mix up AI, Machine Learning, and Deep Learning it helps to clear that up early.
Pick up Python
You don’t need to be an expert coder, but you should be comfortable with the basics. Things like loops, functions… and later, libraries like NumPy and Pandas.
Don’t skip the math
Some linear algebra and basic probability go a long way. You don’t need to go super deep just enough to understand what’s happening behind the scenes.
Move into Machine Learning
This is where things start getting interesting. Learn how models actually work regression, classification, that sort of thing.
Then explore advanced topics
Deep Learning, NLP, maybe even Generative AI. Take it step by step no rush.
Build a few projects
Honestly, this part matters more than people think. Even simple projects (like a chatbot) help everything click.
Get job-ready
At some point, start thinking about your resume, GitHub, and interviews. It sneaks up on you faster than expected.
Best Way to Learn
You can piece all this together on your own, sure but it’s easy to get lost or jump between topics. A structured path makes life easier.
That’s why a lot of beginners go with AI training courses from H2K Infosys. It’s all laid out in one place from basics to advanced topics with projects and career support built in, so you’re not guessing what to learn next.