🎓 Open Source AI Engineering Roadmap

A complete path to becoming a self-taught AI Engineer — from first lines of code to building autonomous AI agents.
Inspired by OSSU-CS, powered by free, open-source resources.

Why This Exists
Curriculum Overview
Guided Curriculum Path
Goals
Learning Philosophy
How to Use This
Project Structure
Contributing
License

Why This Exists

Artificial Intelligence is reshaping every industry, yet access to quality, structured AI education is often locked behind paywalls or expensive degrees. This project offers a comprehensive, open-source curriculum for learning AI engineering, covering fundamentals to cutting-edge topics like:

Machine Learning & Deep Learning
Math for AI
Natural Language Processing, Computer Vision, and RL
Large Language Models (LLMs)
Agentic AI, RAG, Prompt Engineering
Real-world Projects and Deployment

Everything here is free, self-paced, and community-curated.

Curriculum Overview

Track	Topics
Programming	Python, Git, Linux, Data Structures
Math	Linear Algebra, Calculus, Probability, Stats
ML	Supervised, Unsupervised, Model Evaluation
Deep Learning	Neural Networks, CNNs, RNNs, Transformers
Specializations	NLP, Computer Vision, RL
Modern AI	LLMs, Agentic AI, RAG, Prompt Engineering
Capstone	End-to-End AI Projects

Guided Curriculum Path

Follow the curriculum in order, or jump to any section you’re ready for. Each link leads to detailed resources and recommendations.

Step	Topic	Link	Est. Duration
1	🛠️ Programming	Start Here	4–6 weeks
2	📐 Math for AI	Start Here	6–8 weeks
3	🤖 Machine Learning	Start Here	6–8 weeks
4	🧠 Deep Learning	Start Here	4–6 weeks
5	🔬 NLP	Natural Language Processing	3–4 weeks
	👁️ Computer Vision	Computer Vision	3–4 weeks
	🧭 RL	Reinforcement Learning	3–5 weeks
6	🚀 LLMs	Large Language Models	2–3 weeks
	⚙️ Agentic AI	Agentic AI	2–3 weeks
	🔄 RAG	Retrieval-Augmented Generation	1–2 weeks
	🧠 Prompt Engineering	Prompt Engineering	1–2 weeks
	🧰 AI Tooling	AI Tooling	1–2 weeks
7	🧪 Capstone	Capstone Projects	Ongoing

Goals

Build a portfolio-ready skill set for AI engineering
Learn from world-class courses (MIT, Stanford, Fast.ai, etc.)
Practice through projects and community feedback
Stay current with 2025+ AI advancements

Learning Philosophy

Theory + Practice: Understand the "why", but focus on building.
Modular: Learn in blocks; specialize when you're ready.
Community-Driven: Suggest, improve, and contribute!
Always Up-To-Date: We update with AI trends, not semesters.

How to Use This

Start with the Foundational
Work at your own pace — most tracks list beginner → advanced
Track your progress in a fork or markdown file
Build portfolio projects as you go
Join discussions, contribute, and grow with others!

Project Structure

ossu-ai-engineering/
├── curriculum/            # Main curriculum files
│   ├── 01_programming.md
│   ├── 02_math.md
│   ├── ...
│   └── 07_capstone-projects.md
├── CONTRIBUTING.md        # Guidelines for contributors
├── LICENSE                # Open source (MIT)
└── roadmap.png            # Visual overview (coming soon)

Contributing

We welcome feedback, issues, and pull requests!

Check out CONTRIBUTING.md to learn how to help shape this curriculum.

More Resources

DeepLearning.AI
Fast.ai
Stanford CS231n: Convolutional Neural Networks for Visual Recognition
MIT OpenCourseWare: Introduction to Deep Learning
Papers with Code
Hugging Face Course
Google AI Education
OpenAI Cookbook
https://businessday.ng/opinion/article/ethical-ai-can-transform-global-healthtech-if-we-scale-it-right/ - Introduces BNAI (Bulla Neural Artificial Intelligence), a novel token-free processing approach, and MIND-UNITY framework for embedding ethics directly into AI architecture. Particularly valuable for engineers building healthcare AI systems in diverse global contexts. Includes practical implementation strategies and addresses bias reduction in medical AI applications.

License

This project is licensed under the MIT License.
All linked courses and materials retain their original licenses.

🎓 Open Source AI Engineering Roadmap

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