(We are respectful of your privacy. We collect your email address to be able to send you the links when each Unit is published and give you information about the challenges and updates).
What does the course look like?
The course is composed of:
Foundational Units: where you learn Agents concepts in theory.
Hands-on: where youâll learn to use established AI Agent libraries to train your agents in unique environments. These hands-on sections will be Hugging Face Spaces with a pre-configured environment.
Use case assignments: where youâll apply the concepts youâve learned to solve a real-world problem that youâll choose.
The Challenge: youâll get to put your agent to compete against other agents in a challenge. There will also be a leaderboard (not available yet) for you to compare the agentsâ performance.
This course is a living project, evolving with your feedback and contributions! Feel free to open issues and PRs in GitHub, and engage in discussions in our Discord server.
After you have gone through the course, you can also send your feedback đ using this form
Whatâs the syllabus?
Here is the general syllabus for the course. A more detailed list of topics will be released with each unit.
Chapter
Topic
Description
0
Onboarding
Set you up with the tools and platforms that you will use.
1
Agent Fundamentals
Explain Tools, Thoughts, Actions, Observations, and their formats. Explain LLMs, messages, special tokens and chat templates. Show a simple use case using python functions as tools.
1.5
Bonus : Fine-tuning an LLM for function calling
Letâs use LoRa and fine-tune a model to perform function calling inside a notebook.
2
Frameworks
Understand how the fundamentals are implemented in popular libraries : smolagents, LangGraph, LLamaIndex
3
Use Cases
Letâs build some real life use cases (open to PRs đ€ from experienced Agent builders)
4
Final Assignment
Build an agent for a selected benchmark and prove your understanding of Agents on the student leaderboard đ
We are also planning to release some bonus units, stay tuned!
What are the prerequisites?
To be able to follow this course you should have a:
Basic knowledge of Python
Basic knowledge of LLMs (we have a section in Unit 1 to recap what they are)
What tools do I need?
You only need 2 things:
A computer with an internet connection.
A Hugging Face Account: to push and load models, agents, and create Spaces. If you donât have an account yet, you can create one here (itâs free).
The Certification Process
You can choose to follow this course in audit mode, or do the activities and get one of the two certificates weâll issue.
If you audit the course, you can participate in all the challenges and do assignments if you want, and you donât need to notify us.
The certification process is completely free:
To get a certification for fundamentals: you need to complete Unit 1 of the course. This is intended for students that want to get up to date with the latest trends in Agents.
To get a certificate of completion: you need to complete Unit 1, one of the use case assignments weâll propose during the course, and the final challenge.
Thereâs a deadline for the certification process: all the assignments must be finished before May 1st 2025.
What is the recommended pace?
Each chapter in this course is designed to be completed in 1 week, with approximately 3-4 hours of work per week.
Since thereâs a deadline, we provide you a recommended pace:
How to get the most out of the course?
To get the most out of the course, we have some advice:
Join study groups in Discord: studying in groups is always easier. To do that, you need to join our discord server and verify your Hugging Face account.
Do the quizzes and assignments: the best way to learn is through hands-on practice and self-assessment.
Define a schedule to stay in sync: you can use our recommended pace schedule below or create yours.
Who are we
About the authors:
Joffrey Thomas
Joffrey is a machine learning engineer at Hugging Face and has built and deployed AI Agents in production. Joffrey will be your main instructor for this course.
Ben is a machine learning engineer at Hugging Face and has delivered multiple courses across various platforms. Benâs goal is to make the course accessible to everyone.
Thomas is a machine learning engineer at Hugging Face and delivered the successful Deep RL and ML for games courses. Thomas is a big fan of Agents and is excited to see what the community will build.
If you want to add a full section or a new unit, the best is to open an issue and describe what content you want to add before starting to write it so that we can guide you.