Both of these are free with no catch: no locked chapters, no “audit for free but pay for the certificate” pattern, no email gate hiding the good material. They also both assume you can write basic Python. Past that, they are built on genuinely different premises about how people learn, and picking the wrong one first is a common way to lose momentum. This guide walks through what each one actually covers, who each is built for, and how to decide where to start.

What Hugging Face’s free courses actually teach

Hugging Face publishes its courses directly on its own learning platform, and they are structured around the company’s own open source stack rather than deep learning theory in the abstract. The flagship course, generally referred to as the LLM Course (it grew out of what was originally the NLP Course), walks through the Hugging Face ecosystem: the transformers library, the Datasets library, tokenizers, and the Accelerate library for training at scale, all tied together through the Hugging Face Hub where models and datasets are shared.

The early chapters cover how transformer models work well enough to use them confidently: loading a pretrained model, running inference, understanding tokenization, and fine-tuning a model on your own dataset. Later chapters move into more applied territory, including classic NLP tasks like text classification and question answering, and then into topics that matter more for current LLM work, such as working with the Hub, sharing your own fine-tuned models, and building on top of existing checkpoints instead of training from scratch.

The AI Agents Course

Alongside the LLM Course, Hugging Face also offers a free AI Agents Course that covers the more recent shift toward agentic systems: models that call tools, plan multi-step actions, and interact with external systems rather than just generating text. It introduces core agent concepts and has you build with frameworks such as smolagents, LlamaIndex, and LangGraph, working inside Hugging Face Spaces so you can run and share what you build without setting up your own infrastructure.

What ties both courses together is a “use real, current tools” philosophy. You are not building toy neural networks from scratch to prove a point about backpropagation. You are learning the actual libraries and workflows that show up in real NLP and LLM jobs and open source projects right now, which is exactly what makes Hugging Face’s material distinctive.

Who Hugging Face’s courses are best for

Hugging Face’s free courses fit best if you already know roughly what machine learning is and you want to get productive with modern NLP and LLM tooling specifically, rather than deep learning as a broad subject. If your goal is something concrete like “fine-tune a language model for my own dataset” or “understand how to use models from the Hub in a real project,” the LLM Course gets you there directly, without a long detour through general deep learning theory first. It also suits people who are curious about agents and want a structured, code-first introduction rather than piecing one together from scattered blog posts.

It’s a less natural starting point if you have never trained any kind of model before and want to understand deep learning as a field. The course moves quickly into transformer-specific territory, and while it explains what you need along the way, it isn’t trying to be a general deep learning foundation the way fast.ai is.

What fast.ai’s free courses actually teach

fast.ai’s flagship free course is Practical Deep Learning for Coders, created by Jeremy Howard, who co-founded fast.ai with Rachel Thomas. The course is organized as a series of lessons, each built around a real, working model, and it uses PyTorch together with fastai’s own software layer on top of it, which Howard also leads development on. There’s an accompanying book, freely available online, that closely follows the course material for anyone who prefers reading to video.

The lessons cover image classification, natural language processing, tabular data, recommendation systems, and the underlying mechanics of how neural networks actually learn, including building a training loop and stochastic gradient descent from scratch later in the course once you already have the intuition for what those pieces are doing. No specialized hardware is required; the course points you toward free compute options for both training and deploying models, so you can follow along without owning a GPU.

The top-down teaching philosophy, and why it’s the point

What makes fast.ai distinctive isn’t really the topic list, it’s the order. Most technical courses, including most university machine learning courses, teach bottom-up: linear algebra, then calculus, then optimization theory, then finally a working model somewhere near the end of the term. fast.ai deliberately inverts that. You train a real, useful model in the first lesson, then spend the rest of the course peeling back layers to understand why it works.

Jeremy Howard has written directly about this approach, describing it as closer to how people learn sports or music: you play the whole game badly first, then improve specific skills once you have a feel for what they’re for. In fast.ai’s case that means you get a working image classifier or text model running almost immediately, and concepts like gradients, loss functions, and architecture choices get introduced later, tied to problems you’ve already seen firsthand rather than presented as abstract theory you have to trust will eventually be useful.

Who fast.ai is best for

fast.ai fits best if you want an actual foundation in deep learning, not just fluency with one company’s tooling. It suits people who learn better by doing first and reading theory second, and people who have found traditional, math-heavy courses discouraging or slow to get to anything they could actually run. Because the course deliberately avoids requiring university-level math up front, introducing calculus and linear algebra only as specific lessons need them, it’s a genuinely reasonable starting point even if your last math class was a while ago.

It’s a less direct fit if you already understand deep learning basics and specifically want to get productive with the transformers library or the Hugging Face Hub. You can absolutely get there through fast.ai eventually, since the course does cover NLP, but it isn’t organized around one company’s ecosystem the way Hugging Face’s course is.

Hugging Face vs fast.ai: how to choose

If you’re unsure, ask yourself what you actually want to be able to do in three months. If the answer is something specific involving language models, fine-tuning, or agents, Hugging Face gets you there faster because it’s built around exactly that. If the answer is closer to “I want to understand deep learning and build things with it,” fast.ai is the better first stop, because its whole structure is designed to build that understanding rather than assume you already have it. Our broader roundup of free generative AI and LLM courses and free deep learning, NLP and computer vision courses covers other options if neither one is quite the right fit.

What background you genuinely need

Both courses assume you can read and write basic Python, but “basic” means slightly different things for each.

• For Hugging Face’s LLM Course: you should be comfortable with Python fundamentals, functions, loops, working with lists and dictionaries, and ideally have touched a library like NumPy or pandas before. Prior exposure to machine learning concepts helps you move faster but the course explains transformer-specific ideas as it introduces them.
• For fast.ai’s Practical Deep Learning for Coders: the bar is intentionally similar, general coding experience in Python and comfort using a Jupyter notebook. No prior machine learning or deep learning background is assumed, and no university-level math is required going in.
• Neither course requires you to already know PyTorch: fast.ai teaches it as part of the course, and while Hugging Face’s transformers library also supports PyTorch underneath, you don’t need to have used it directly beforehand.
• If you’ve never written Python at all: spend a couple of weeks on the basics first. Our guide to free TensorFlow and PyTorch courses is a reasonable next stop once you’re comfortable with core Python and want to go deeper on the underlying frameworks.

You don’t have to pick just one

In practice a lot of people end up touring both, just not at the same time. A common pattern is starting with fast.ai to build real intuition for how deep learning works, then moving to Hugging Face’s LLM Course once you want to specialize in transformer models and the Hub-based workflow that dominates NLP work today. The reverse order works too, especially if you already have some deep learning background and just want the current NLP and LLM tooling. Neither course is trying to be everything, and that’s part of why both are worth your time rather than a reason to pick a side.

Frequently asked questions