A structured, self-paced roadmap for going from complete beginner to job-ready Machine Learning Engineer —
built in public, one topic at a time.
This is my personal ML Engineer roadmap — a living repository where I document everything I learn on the journey from zero to becoming a Machine Learning Engineer.
Every folder is a phase. Every file is a topic. Every resource is something I've personally vetted.
If you're on a similar path, feel free to fork it and make it your own.
flowchart LR
A[ Math] --> B[ Python]
B --> C[ SQL]
C --> D[ Machine Learning]
D --> E[ Deep Learning]
E --> F[ Software Engineering]
F --> G[ MLOps]
| Phase | Topics | Status |
|---|---|---|
| 01 - Math | Linear Algebra, Statistics, Calculus | 🔄 In Progress |
| 02 - Python | Data Structures, OOP, NumPy, Pandas | 🔄 In Progress |
| 03 - SQL | Queries, Joins, Aggregations | ⬜ Not Started |
| 04 - Machine Learning | Regression, Trees, Clustering, Evaluation | ⬜ Not Started |
| 05 - Deep Learning | Neural Networks, CNNs, RNNs, Transformers | ⬜ Not Started |
| 06 - Software Engineering | DSA, System Design, APIs | ⬜ Not Started |
| 07 - MLOps | Cloud, Docker, Git & CI/CD | ⬜ Not Started |
Every topic folder contains two files:
notes.md— my personal summary, key concepts, and understanding written in Obsidianresources.md— curated list of courses, books, and free resources
| Purpose | Tool |
|---|---|
| Note-taking | Obsidian |
| Language | Python |
| ML Libraries | Scikit-learn, PyTorch |
| Data | NumPy, Pandas, Matplotlib |
| Version Control | Git & GitHub |
Built with curiosity, coffee, and a stubborn refusal to stay a beginner.
⭐ Star this repo if it helps you too