A collaborative, structured, and hands-on learning vault for Machine Learning — built by students, for students.
We follow a curated roadmap based on Hands-On Machine Learning and other top resources, focusing on shared progress, real understanding, and community-driven learning.
This repository is a living knowledge base for learning ML together.
- 📚 Covers core ML topics with practical focus
- 🧠 Includes math and Python foundations
- 🛠️ Features projects, notes, exercises, and tools
- 🗂️ Organized using Obsidian
- 🔄 Built to be shared, expanded, and contributed to by a group
- 🧱 Powered by Quartz — customized for this ML learning vault.
We maintain a central 📍Roadmap to track progress, topic links, learning status, and assigned tasks.
Each topic folder contains:
01-Overview.md– what the topic is about02-Resources.md– recommended links and materials03-CodeExamples/– code notebooks or demos04-Notes.md– handwritten or collaborative notes05-Exercises.md– problems and solutions06-Discussion.md(optional) – team thoughts or questions
| Folder | Purpose |
|---|---|
00-General |
Roadmap, repo structure, contribution notes |
01-Topics |
Learning content by topic (math, ML, DL, etc.) |
02-Papers |
Research papers and summaries |
03-Projects |
Hands-on projects and experiments |
04-Meetings |
Meeting notes, tasks, and planning |
05-Templates |
Reusable templates (e.g., for meetings, overviews) |
06-CheatSheets |
Quick reference guides and tips |
07-Resources |
Datasets, tools, and useful external resources |
Everyone is welcome to contribute. This is a group-driven space, not a one-person show.
- Follow the existing topic structure when adding new content.
- Prefer pull requests with clear commit messages.
- Be kind and supportive. We're all learning.
This repository is open for educational use. Please respect content sources and contributors.