By the TechStudio editorial team · Updated September 25, 2026 · Editorial policy
A practical, hands-on collection of open-source repositories for learners who want to move beyond tutorials and study how AI agents, Retrieval-Augmented Generation, large language models, deep learning, and machine learning systems are implemented in code.
Why these repositories?
Instead of only watching videos, inspect project structure, configuration, notebooks, prompts, retrieval code, model code, evaluation logic, and documentation.
Fork or clone a repository, reproduce one example, change one component, and document what you learned. That turns passive study into practical evidence.
The list spans beginner ML notebooks, RAG techniques, agent orchestration, LLM implementation, PyTorch, and production-oriented ML engineering.
Use the repositories as references, then create your own implementation or extension. Clearly distinguish your work from the original project and explain your engineering decisions.
The 8 repositories
Open each repository to inspect the source, README, notebooks, examples, issues, and project history.
microsoft/ai-agents-for-beginners
A hands-on curriculum for getting started with AI agents. Use it to understand the building blocks of agentic applications and progressively connect concepts to working code.
NirDiamant/agents_towards_production
A practical reference for understanding the building blocks that matter when moving an agent beyond a simple demo. Study the architecture, components, and engineering considerations used in production-oriented agent stacks.
NirDiamant/RAG_Techniques
A broad collection for studying Retrieval-Augmented Generation patterns. Use it to compare retrieval strategies, augmentation approaches, evaluation ideas, and implementation techniques across different RAG designs.
jamwithai/production-agentic-rag-course
A hands-on resource for exploring RAG and agentic AI projects. Use it to connect retrieval with agent behavior and understand how an application can combine multiple AI components into a practical system.
HandsOnLLM/Hands-On-Large-Language-Models
A code-first companion for learning how language models work and how to experiment with them. Work through notebooks and connect foundational LLM concepts to practical implementation and fine-tuning workflows.
mrdbourke/pytorch-deep-learning
A structured code and notebook resource for learning PyTorch and deep learning. The repository is useful for understanding tensors, neural networks, training workflows, computer vision foundations, and practical model-building patterns.
GokuMohandas/Made-With-ML
A practical resource for understanding the broader machine-learning product lifecycle. Study how data, experimentation, training, testing, deployment, CI/CD, and monitoring fit together when building an ML system.
microsoft/ML-For-Beginners
A beginner-oriented curriculum for building core machine-learning knowledge through practical lessons and notebooks. Use it to establish fundamentals before moving deeper into modern AI engineering.
Suggested learning path
Start with Microsoft’s ML for Beginners if you need a structured introduction. Then use the PyTorch repository to strengthen deep-learning implementation skills.
Use Hands-on Large Language Models to understand language-model concepts and implementation patterns before jumping into more complex agent systems.
Study the RAG Techniques repository and reproduce multiple retrieval patterns. Pay attention to chunking, embeddings, retrieval, reranking, context construction, evaluation, and failure cases.
Use AI Agents for Beginners first, then study production-oriented agent systems and agentic RAG. Compare tool use, planning, memory, retrieval, orchestration, evaluation, and failure handling.
Use Made With ML to understand how experimentation becomes an engineered system: data validation, reproducibility, testing, deployment, CI/CD, and monitoring.
Portfolio & interview value
Be ready to explain embeddings, vector search, keyword search, chunking, metadata filters, reranking, context limits, retrieval evaluation, generation evaluation, citations, and common failure modes.
Understand tool calling, planning, state, memory, orchestration, retries, guardrails, observability, evaluation, and how an agent differs from a simple prompt-and-response application.
Connect model development with data quality, experiments, tests, deployment, CI/CD, monitoring, reproducibility, and system reliability.
Do not simply list a repository you studied. Build an original project or meaningful extension and describe the architecture, technologies, measurable result, and your individual contribution.
These repositories are learning references. When creating portfolio projects, respect each project’s license and attribution requirements, avoid presenting someone else’s repository as your own, and verify the current README and repository instructions before running code.
Start building
A focused implementation is more useful than trying to finish all eight at once. Start with one repo, reproduce one example, make one improvement, and document what changed.