This repository contains my notes and small code examples as I learn Python concepts that are important for machine learning. The focus isn’t on ML models or libraries. It’s on how Python actually behaves in places that matter for ML-style code—data handling, object behavior, control flow, and how execution really works. This is an active, working repository. As my understanding improves, older material may be revisited, rewritten, or removed.
The goal is to build a solid Python foundation for machine learning, not to present finished projects or polished demos. I’m using this repo to work through Python concepts from a reasoning-first point of view, connect language features to how they show up in ML workflows, keep examples small and focused rather than clever, and prioritize understanding over speed or completeness.
Core Python syntax and semantics, data structures and how they behave, functions and scope, execution flow, mutability and references, basic object model ideas, and iteration or control patterns that come up often in data processing and ML code.
This repo does not cover machine learning algorithms or models, deep learning frameworks (NumPy, pandas, PyTorch, etc.) for now, large scripts or end-to-end projects, competitive programming, or step-by-step beginner-style tutorials. Those topics are better handled in separate, more focused repositories.
The repository is organized by concept. Each folder focuses on a single idea and usually includes a short README explaining why the concept matters for ML, along with one or more small Python files that demonstrate it clearly. The structure is meant to grow gradually as new concepts are added.