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
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.
| 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 |
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 |
- 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
- 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.
- 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!
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)
We welcome feedback, issues, and pull requests!
Check out CONTRIBUTING.md to learn how to help shape this curriculum.
- 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.
This project is licensed under the MIT License.
All linked courses and materials retain their original licenses.