The most reliable and practical resources come straight from the organizations developing these technologies, and they are all free. If you want to focus on what matters, build real skills, and keep up with the latest developments, here’s a list of the best free resources for learning LLMs, AI Agents, and GenAI. I recommend these to my mentees and use them myself to stay current.
Best Free Resources to Learn LLMs, AI Agents, and GenAI
When I review a learning resource, I focus on real technical depth, not hype. I look for actual code, clear explanations of the math when needed, and practical deployment strategies. These five resources meet all those criteria.
1. Hugging Face LLM Course
If you want to really understand how Large Language Models work, move beyond basic API wrappers and explore the Hugging Face ecosystem.
The Hugging Face LLM Course is a great open-source program that covers the basics of transformers. It goes beyond just calling an API and shows you how to load models, handle tokenization, and do parameter-efficient fine-tuning (PEFT) with methods like LoRA and QLoRA.
I tell my mentees that to be taken seriously as an ML Engineer today, you need to know how to work with open-weights models on your own machine or dedicated hardware. This course is a great place to start. Find it here.
2. Hugging Face AI Agents Course
Generating text is just the beginning. The industry is quickly shifting toward AI that can take action. LLMs are becoming active AI Agents that can reason, plan, and use external tools to get things done.
Hugging Face now offers a dedicated AI Agents course that explains this shift. It shows you how to let an LLM use external APIs, such as web search, calculators, or custom databases, and how to organize agent reasoning with frameworks like ReAct (Reasoning and Acting).
What I like most about this resource is how practical it is. It avoids buzzwords and gives you the exact Python code you need to make an LLM trigger a function call. Find it here.
If you want to learn even more about LLMs and AI agents, I recommend Hands-on GenAI, LLMs and AI Agents. It’s a practical guide that helps you understand the concepts and build real-world applications.
3. Agentic AI on Google Cloud
Hugging Face is great for open-source and local development, but it’s also important to know how to deploy these systems at an enterprise level. When I work on production systems, I often use cloud infrastructure.
Google’s “Agentic AI on Google Cloud” course connects agentic theory with real-world enterprise needs. It teaches you how to use Vertex AI to build, deploy, and scale AI agents. You’ll learn to connect agents with large enterprise datasets, manage cloud security, and use managed infrastructure to handle thousands of agent tasks at once.
If you want to work at a top tech company, knowing how agents work in a major cloud platform like GCP is a big advantage. Find it here.
4. My List of 50 Real-World AI Projects
Theory and tutorials can only take you so far. The biggest mistake I see new AI engineers make is getting stuck in tutorial hell, watching endless videos but never writing their own code. To get hired, you need a portfolio.
To help with this, I put together a list of 50 Real-World AI Projects. This list is meant to challenge you and help you grow. It includes projects like building end-to-end Retrieval-Augmented Generation (RAG) pipelines for document search, developing AI Agents, and fine-tuning custom models for specific industry needs.
By working through these projects, you’ll learn to debug real errors, handle messy data, and deal with the practical challenges of AI deployment that most courses don’t cover. Find it here.
5. Anthropic Academy
Finally, there’s Anthropic Academy. Anthropic, the creators of Claude, takes a careful and safety-focused approach to AI development. Their academy is the best place I’ve found to learn advanced technical prompt engineering.
This goes beyond simple “act like a professional writer” prompts. Anthropic shows you how to use XML tags to structure outputs, do multi-shot prompting for complex reasoning, and manage very large context windows (up to 200,000 tokens).
Whenever I have trouble getting a model to produce well-structured JSON for a complex production pipeline, the techniques from Anthropic’s documentation always help. Find it here.
How to Use These Resources
So, how can you use these resources in your daily learning?
Don’t try to learn everything at once. Begin with the Hugging Face LLM course to build your foundation in transformers and fine-tuning. Once you understand the basics, move on to Anthropic Academy to master advanced prompting.
When you’re ready to build something interactive, try the Hugging Face AI Agents course to learn about tool-calling. Next, check out Google Cloud’s Agentic AI resources to see how you could scale your agent for many users. As you go, keep my list of 50 Real-World AI Projects handy. Choose a project that matches what you’ve just learned and build it from the ground up.
The Takeaway
So, here are the best free resources to learn LLMs, AI Agents, and GenAI:
- Hugging Face LLM Course
- Hugging Face AI Agents Course
- Agentic AI on Google Cloud
- My List of 50 Real-World AI Projects
- Anthropic Academy
Be consistent, write code every day, and focus on building real solutions instead of getting distracted by the hype.
I hope you found this article on the best free resources for learning LLMs, AI Agents, and GenAI helpful. For more AI and machine learning tips, you can follow me on Instagram.
