wtf is dm-ml?

This repository is a collection of Jupyter Notebooks designed to teach beginners the fundamentals of Python as it applies to AI and machine learning. Think of it as a self-paced tutorial that walks you through key concepts using hands-on examples you can run and experiment with directly in your browser or local environment. Jupyter Notebooks are interactive documents that mix code, explanations, and output all in one place. Instead of reading a textbook or watching a video, you write and run small chunks of Python code, see the results immediately, and learn by doing. This repository gathers several of these notebooks together, covering the foundational Python skills you'd need before diving into actual machine learning projects. The intended audience is people brand new to both Python and AI, no prior coding experience assumed. If you're someone who wants to understand how machine learning actually works, or you're exploring whether AI is something you want to pursue professionally, this is a low-friction entry point. Rather than installing complex tools or wrestling with setup, you can open a notebook and start writing Python code in minutes. The README itself is fairly minimal and doesn't spell out the exact topics covered, so you'd need to explore the notebook files to see what's inside. That said, the emphasis on "best practises" suggests the notebooks aren't just teaching syntax for its own sake, they're modeling good habits and approaches that will serve you well as you grow as a programmer.