Can you explain a beginner-friendly roadmap to learn artificial intelligence?

Getting into AI can feel like… a lot. But once you break it into chunks, it’s not as bad as it looks.

• Start with the basics: Pick up Python first that’s kind of your main tool. At the same time, try to understand some basic statistics and a bit of linear algebra. You don’t need to go super deep here, just enough to not feel lost later.
• Understand the core concepts: You’ll run into terms like supervised learning, regression, classification all the time. It might feel repetitive, but this stuff really is the foundation.
• Actually use the tools: Reading alone won’t help much. Try working with NumPy, Pandas, Scikit-learn—mess around, make mistakes, fix them. That's when the pieces begin to fall into place.
• Take your time with deep learning. There's no need to hurry. But when you're ready, begin to investigate neural networks and the frameworks that support them, such as TensorFlow or PyTorch.
  It’s a bit of a jump, but manageable.
• Build small projects: Doesn’t have to be anything fancy. Even a simple prediction model or a basic chatbot teaches you a lot more than you’d expect.
• See what you enjoy: Some people end up liking NLP, others go toward computer vision. You’ll figure that out naturally over time.
• Think about an AI certification program: Not a must, but having some structure can save you from a lot of random trial and error. If you’re considering one, H2K Infosys has a pretty practical AI certification program that’s beginner-friendly and focused on real-world skills.