You pick up practical AI engineering faster by digging into real code, real notebooks, and real systems. I’ve learned more by breaking stuff and fixing it than by watching one more lecture. These repos are free, well-structured, and focused on what actually works in practice, helping you move from theory to shipped features.
Each pick includes hands-on materials and clear guidance. You’ll find lessons, notebooks, examples, and end-to-end projects that run without yak-shaving. Let’s walk through the 10 repos and how they help you build useful AI systems.
1. Hands-On Large Language Models
It’s the full code from the book, with notebooks covering LLM basics, training, and fine-tuning. If you like a guided, notebook-first path from foundations to customization, this feels like a friendly trail map. Link: https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
2. AI Agents for Beginners (Microsoft)
A free, structured 11-lesson course to start AI agents the right way. Think of it as turn-by-turn directions for agents, minus the detours and dead ends. Link: https://github.com/microsoft/ai-agents-for-beginners
3. GenAI Agents
Clear tutorials and implementations of generative AI agent techniques, from basic builds to advanced strategies. You’ll see how different agent strategies are wired up, which makes design choices feel obvious. Link: https://github.com/NirDiamant/GenAI_Agents
4. Made With ML
One of the best resources for building production-grade ML systems end to end. My pick when you care about real-world systems and operational quality, not just pretty notebooks. Link: https://github.com/madewithml/basics
5. Prompt Engineering Guide
A massive collection of guides, papers, notebooks, and resources on prompt engineering. Keep it handy when you want proven patterns and quick references in one place. Link: https://github.com/dair-ai/Prompt-Engineering-Guide
6. Hands-On AI Engineering
Curated examples of AI-powered applications and agentic systems using LLMs that actually run. It shows how the pieces snap together in working examples, which saves a lot of guesswork. Link: https://github.com/Sumanth077/Hands-On-AI-Engineering
7. Awesome Generative AI Guide
A one-stop repo for GenAI research updates, notebooks, interview prep, and more. Great for staying current while practicing with solid reference materials you can trust. Link: https://github.com/aishwaryanr/awesome-generative-ai-guide
8. Designing Machine Learning Systems (Resources)
Summaries and references for one of the most important ML systems books out there. It strengthens your systems thinking, which quietly prevents half the headaches before they begin. Link: https://github.com/chiphuyen/dmls-book
9. Machine Learning for Beginners (Microsoft)
A beginner-friendly ML curriculum with practical examples and exercises you can actually finish. A solid starting point if you’re new to ML and want quick wins. Link: https://github.com/microsoft/ML-For-Beginners
10. LLM Course
A hands-on, end-to-end course on building, evaluating, and deploying LLM applications. Ideal when you want a clear path from spark of an idea to deployment. Link: https://github.com/mlabonne/llm-course
For even more hands-on repos, explore this curated list of open-source AI projects.
These resources focus on what matters: practical skills, working examples, and clear steps. Pick one that fits your level and goal, set a tiny deadline, and move through the materials with intent.
You can continue by exploring AI source code repositories and examples to build on these projects and deepen your practice.
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