Artificial Intelligence (AI) engineering has become one of the most in-demand career paths, combining skills in machine learning, deep learning, and large language models (LLMs) to build intelligent systems. Whether you’re a beginner just stepping into the AI space or a developer looking to advance your expertise, the internet offers countless free learning resources but finding structured, high-quality content can be challenging.
To make the journey easier, here’s a step-by-step, completely free roadmap with 10 GitHub repositories designed to help you master AI fundamentals, work on hands-on projects, and gain production-ready skills. Each resource offers practical guidance, from classical machine learning to advanced AI agent development, making it easier to grow your knowledge and portfolio.
Table of Contents
1. Machine Learning for Beginners
A 12-week project-based curriculum that introduces classical machine learning concepts using Scikit-learn and real-world datasets. It includes lessons, quizzes, and hands-on projects to help solidify your understanding. Ideal for those who want to start with the foundations of ML.
GitHub Link – https://github.com/microsoft/ML-For-Beginners
2. AI for Beginners
A beginner-friendly repository covering essential AI topics such as neural networks, natural language processing (NLP), computer vision (CV), transformers, and AI ethics. It includes labs in PyTorch and TensorFlow, allowing you to learn through practical implementation.
GitHub Link – https://github.com/microsoft/AI-For-Beginners
3. Neural Networks: From Zero to Advanced
Once you’ve learned the basics, it’s time to dive deeper into deep learning. This repository teaches how to build modern neural networks from scratch, including transformer-based models and GPT-like architectures, helping you understand the core mechanics of today’s AI systems.
GitHub Link – https://github.com/karpathy/nn-zero-to-hero
4. Deep Learning Paper Implementations
Learn how top AI architectures work by studying PyTorch implementations of over 60 influential research papers. This includes models for transformers, generative adversarial networks (GANs), diffusion models, and more. Perfect for those who want to bridge the gap between theory and practice.
GitHub Link – https://github.com/labmlai/annotated_deep_learning_paper_implementations
5. From ML Models to Production
Transitioning from experiments to production-ready models is a key skill for AI engineers. This repository teaches how to design, develop, deploy, and maintain real-world ML systems using MLOps, CI/CD, and industry best practices.
GitHub Link– https://github.com/GokuMohandas/Made-With-ML
6. Hands-on Large Language Models
A comprehensive visual guide to LLMs covering tokenization, fine-tuning, and retrieval-augmented generation (RAG). It offers practical exercises that help you apply LLM concepts to real-world problems, making it an excellent resource for intermediate learners.
GitHub Link – https://github.com/HandsOnLLM/Hands-On-Large-Language-Models
7. Advanced RAG Techniques
Once you understand RAG basics, this repository dives into 30+ advanced methods for improving speed, accuracy, and efficiency, such as Hybrid Search, HyDE, and Graph-based RAG. These techniques can significantly enhance the capabilities of AI-powered search and knowledge systems.
GitHub Link – https://github.com/NirDiamant/RAG_Techniques
8. AI Agents for Beginners
An introductory course on building AI agents that can plan, reason, and execute tasks autonomously. You’ll explore agent design principles and learn how to integrate them with existing AI frameworks for real-world applications.
GitHub Link– https://github.com/microsoft/ai-agents-for-beginners
9. AI Agents for Production Deployment
This resource covers the full lifecycle of deploying AI agents, from memory management and orchestration to security and scalability. It acts as a practical playbook for moving from prototypes to production-ready AI systems.
GitHub Link – https://github.com/NirDiamant/agents-towards-production
10. AI Engineering Project Hub
A vast collection of 70+ real-world AI projects, tutorials, and templates. It includes examples for LLM-powered apps, RAG systems, and AI agents, giving you endless inspiration and starting points for your own portfolio.
GitHub Link – https://github.com/patchy631/ai-engineering-hub
Conclusion
Mastering AI engineering requires a blend of theoretical knowledge and practical skills. These 10 GitHub repositories form a complete learning path from foundational ML to advanced AI agents enabling you to build projects, experiment with new techniques, and prepare for a successful AI career.
The best part is that all of these resources are completely free, making them accessible to anyone passionate about AI. By dedicating consistent time to exploring, practicing, and implementing the concepts from these repositories, you can develop the expertise needed to thrive in one of the most in-demand fields of the future. Whether your goal is to become an AI researcher, developer, or solutions architect, this roadmap provides a solid foundation to help you stand out in the competitive AI job market.
Related Reads
- Evaluating Large Language Models: Metrics, Best Practices and Challenges
- Synthetic Data in Machine Learning: Proven Benefits, Risks and Use Cases
- Simplifying the Mathematics of Neural Networks and Deep Learning
- LLM Engineer Toolkit – Your Complete Map to 120+ LLM Libraries
- Prompt Engineering vs. Fine-Tuning: Choosing the Right Strategy for Optimizing LLMs in 2025
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