The best free fast.ai AI courses, curated
fast.ai, founded by Jeremy Howard and Rachel Thomas, is famous for teaching deep learning top-down: you train a working, state-of-the-art model in lesson one, then peel back the layers to understand why it works. Everything — the courses, the videos and the book — is completely free.
These are curated links to courses published freely by fast.ai (on course.fast.ai, ethics.fast.ai and GitHub). All content belongs to fast.ai and its instructors. AnybodyCanAI is not affiliated with, sponsored by, or endorsed by fast.ai. The fast.ai name is used only to identify the source of each course.
Practical Deep Learning for Coders
Train a real model in lesson one
Jeremy Howard · 2022 edition · Deep Learning
Summary
A free, code-first deep-learning course for people who can already program. Across nine lessons you build and deploy image, text and tabular models, then work down to the foundations — how SGD, neural nets and the training loop really work.
The most approachable serious deep-learning course anywhere. It gets you shipping working models fast, which keeps motivation high, and the whole thing — videos, notebooks and the full book — is free. The best hands-on complement to theory-first courses.
Schedule
Access
All lessons are free: videos, runnable Kaggle notebooks, and the full text of the fastai book. No signup required.
Materials & links
Source: fast.ai — all links open on the provider's own site.
Deep Learning Foundations to Stable Diffusion
Part 2 — build modern generative models from scratch
Jeremy Howard, Jonathan Whitaker, Tanishq Abraham · 2022–2023 · Deep Learning
Summary
The advanced sequel: implement deep learning (and Stable Diffusion) from the foundations up — matrix multiplication, backprop, the training framework, then diffusion models, attention and transformers — over 25+ in-depth lessons.
One of the few courses that builds a modern generative model essentially from scratch. If you finished Part 1 and want to truly understand what is under the hood, this is the path.
Schedule
Lecture topics and dates rotate each offering — we link straight to fast.ai's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
All lessons and notebooks are free on the course site. No signup required.
Materials & links
Source: fast.ai — all links open on the provider's own site.
Practical Data Ethics
The ethics every AI practitioner should know
Rachel Thomas · 2020 (USF Data Institute) · Data Ethics
Summary
A practical, case-driven course on the ethics of building with data and AI: disinformation, bias and fairness, privacy and surveillance, the tech ecosystem, and algorithmic harms.
Ethics is usually an afterthought in ML courses. This one puts it front and centre with real, recent cases — essential grounding for anyone building AI that affects people.
Schedule
Lecture topics and dates rotate each offering — we link straight to fast.ai's official schedule and syllabus so it's always current.
Full schedule & syllabusAccess
All lesson videos and the syllabus are free and public on ethics.fast.ai.
Materials & links
Source: fast.ai — all links open on the provider's own site.