fast.ai

Explore 4 courses from fast.ai covering AI and machine learning.

4 courses4.7 avg rating690K+ learners

About fast.ai

fast.ai is a non-profit deep learning research and education group founded in 2016 by Jeremy Howard (former President and Chief Scientist of Kaggle) and Rachel Thomas, with the stated goal of democratizing deep learning. Its flagship offering, the free 'Practical Deep Learning for Coders' MOOC, teaches a code-first, top-down approach using PyTorch, the fastai library, and Hugging Face, getting learners to train and deploy real models from the very first lesson. The course is entirely free with no paywalls or upsells, but it grants no certificate to online learners (only the original in-person University of San Francisco cohorts could earn one). fast.ai is widely regarded as one of the best hands-on deep learning resources available, while drawing consistent criticism for its thin theoretical/mathematical coverage and the heavy abstraction of its own fastai library.

Best for: Working programmers with roughly a year of Python experience who learn best by building. It suits people who want to train image classifiers, NLP models, and other deep learning applications quickly, deploy them early, and pick up the underlying theory incrementally rather than front-loading months of math.

Look elsewhere if: Complete programming beginners, and learners who need rigorous, bottom-up mathematical foundations (backpropagation, linear algebra, the internals of frameworks) before applying them. Anyone who specifically needs an accredited certificate or formal credential for a resume should look elsewhere, and those wanting Andrew Ng-style theoretical depth may find the coverage too shallow.

Pricing: Free. The entire 'Practical Deep Learning for Coders' MOOC (Part 1 and Part 2) is available at no cost as recorded video lessons plus interactive notebooks, with no subscription, per-course fee, or audit/paywall split. The companion book 'Deep Learning for Coders with fastai & PyTorch' is sold separately but the notebooks behind it are also free.

Certificates: None for online learners. fast.ai does not issue a certificate of completion for the MOOC; historically only students attending the in-person University of San Francisco classes could earn a University of San Francisco certificate. The course's value is reputational and portfolio-driven rather than credential-driven: employers and the ML community recognize the fast.ai name and the projects/models learners build, but there is no shareable certificate to put on a resume or LinkedIn.

Strengths

Completely free with no paywalls, upsells, or paid tiers; all video lessons, Jupyter notebooks, pretrained models, datasets, and an active forum are open access (course.fast.ai).
Code-first, top-down 'whole game' pedagogy gets learners training and deploying production models (image classification, NLP) in lesson 1, which Reddit and Class Central reviewers repeatedly cite as highly motivating.
Taught by Jeremy Howard, who has ~30 years of ML experience and was the top-ranked global Kaggle competitor two years running, lending strong instructor credibility.
Uses current, industry-relevant tooling (PyTorch, fastai, Hugging Face Transformers, Gradio) and minimal math prerequisites, introducing needed calculus/linear algebra as the course goes.
Strong track record and reputation: fast.ai students won the CIFAR-10 DAWNBench challenge against major tech companies, and alumni report Kaggle medals, published papers, and ML job offers.

Weaknesses

No certificate or formal credential for online learners; only the original in-person University of San Francisco classes could grant one.
Theoretical and mathematical depth is thin by design; reviewers note it 'glazes over' concepts like backpropagation, often requiring outside study (e.g. Khan Academy) to keep up.
Heavy reliance on the fastai library's high-level abstractions and terse, abbreviated variable names (e.g. ds_tfms, pat) can obscure what is happening underneath and hurts code readability for beginners.
Polarizing top-down format and fast pace do not suit everyone; some learners struggle with environment/GPU setup and report difficulty retaining material, preferring a more structured bottom-up course.