Practical Deep Learning for Coders, Reviewed: Free, Excellent, Frozen in 2022

Practical Deep Learning for Coders costs $0, issues no certificate, and is still the 2022 recording. It remains the best free deep learning course for people who already code: 9 lessons of roughly 90 minutes each, taught by Jeremy Howard, built on PyTorch and the fastai library. Our rating is 8/10 for a programmer who wants deep learning to finally click — and about 4/10 if you need a credential or want to work on LLMs.

What follows is a consensus read, not a completion badge. We haven’t sat through all thirteen hours and called that objectivity. What we did do: read fast.ai’s own course pages, the Part 2 syllabus, its homepage and blog index, the archived 2018 course site, the forum thread where the certificate question got settled, DataCamp’s April 2026 course round-up, and five r/learnmachinelearning threads dated July 2022 through November 2025. Every figure in the table below was re-checked against its live source on August 5, 2026 — bar the study-time estimate, which only exists on the archived 2018 site. The table says which is which.

Cost & Duration

There is no billing trick to warn you about here, which is refreshing after every other review on this site. The cost is your attention. The 2018 edition’s “10 hours a week for 7 weeks” is the only study-time figure fast.ai has ever published, and it’s a fair anchor: budget somewhere near 60 hours if you actually run the notebooks, and a third of that if you only watch. DataCamp’s April 2026 round-up independently puts both parts at about 70 hours, which lands in the same neighbourhood.

Curriculum: what’s actually taught

Part 1 runs eight numbered lessons plus a bonus, ordered by what you can build rather than by what’s foundational.

1. Getting started. You train an image classifier in the first session, on data you collect yourself.
2. Deployment. You put that model on the internet. By the end of lesson 2 you have shipped something.
3. Neural net foundations: what the layers are actually doing.
4. Natural language (NLP): Hugging Face Transformers, applied to detecting similar concepts in patent applications.
5. From-scratch model, meaning you write the training loop yourself.
6. Random forests. The non-neural half of practical ML, which most deep learning courses skip entirely.
7. Collaborative filtering: embeddings and recommendations.
8. Convolutions (CNNs).
9. Bonus lesson on data ethics, which fast.ai counts toward its nine.

Part 2 is a different animal: 17 lessons, numbered 9 through 25, implementing Stable Diffusion from scratch. “From scratch” is meant literally — fast.ai’s phrasing is that you “rely on nothing other than Python and its standard library.” You build your own framework (miniai), your own optimisers, your own U-Nets, and you learn to read research papers along the way. It is genuinely advanced, and fast.ai sets the bar in writing: finish Part 1 first, or else already be comfortable writing an SGD training loop from scratch, competing in Kaggle competitions, and working in PyTorch and fastai. That floor is far above Part 1’s, and the two parts get sold as one course far too often.

The teaching order is the whole product. Howard’s stated method is to show a working, state-of-the-art model solving a real problem, then dig down into how it was made, then how the tools that made it were made. Nothing starts with algebra.

Strengths

The teacher is not a content marketer. Jeremy Howard was the top-ranked global competitor on Kaggle two years running, then became its president and chief scientist, then founded Enlitic. He leads fastai development. Those are checkable facts about a person, which is more than most course landing pages give you. fast.ai also displays a Peter Norvig endorsement: the book “delivers on the claim” that deep learning is for everyone, over the byline “Director of Research, Google”. Weigh that one as what it is, a cover blurb the organisation chose to put on its own page. Howard’s competition record is the part that doesn’t need fast.ai’s permission to check.

The top-down order converts people who bounced off everything else. “I tried Andrew NG’s course and found it be too dry. then i tried Fast.ai and it was much more fun since i got to build application from day 1,” an r/learnmachinelearning member wrote on 22 July 2022, the day after the rewritten edition landed. fast.ai promises a built and deployed model by the end of lesson 2, and delivers it. That is a retention mechanism, not a gimmick.

It removes the hardware excuse. fast.ai actively tells you not to train on your own machine unless you enjoy CUDA driver archaeology, and routes you to free Kaggle and Paperspace notebooks instead. The book is free online as executable Jupyter notebooks, every chapter of it. Help routes to forums.fast.ai, which the course page still points you to. It doesn’t say how busy the place is in 2026, and we wouldn’t assume. Total friction to start: one click on lesson 1.

The fit, when it fits, is exact. “I personally love fast.ai courses. But I think they’re for people just like me: people who can code decently but are not developers who nevertheless find themselves in positions where knowing a little AI can help a lot,” one learner posted in 2025. That’s the buyer. Part 1 turns a competent programmer into someone who can train, interpret, and deploy a model without needing a graduate degree first.

Weaknesses

Every fast ai course review lands on the same two arguments: the date, and the wrapper. Both are real. Two more matter as much.

It has not been re-recorded since 2022. fast.ai called the July 2022 release “a complete from-scratch rewrite” that had been two years in the making. It was also the last one. Part 1 is still that University of Queensland recording, and Part 2’s frontier is still the original Stable Diffusion release. Four years, in this field.

The organisation has visibly moved. fast.ai announced in November 2024 that it was joining Answer.AI and building a new kind of course, How To Solve It With Code; that course shipped in October 2025 and now sits first in the two-item course list on fast.ai’s homepage, at solve.it.com. Nobody has promised a Practical Deep Learning refresh. The blog is not dormant — its newest post is dated February 2026 — but run down the post titles and the last Practical Deep Learning release announcement is still the one from July 2022, after 2018 and 2019 editions that arrived a year apart. If you want current LLM engineering, agents, or post-2022 architectures, this course doesn’t have them and doesn’t claim to.

Which is worth knowing, because other reviewers get it wrong. DataCamp’s April 2026 AI-course round-up credits Practical Deep Learning with “2024–2025 updates” covering “modern architectures, fine-tuning workflows.” There are no 2024–2025 updates. The lesson index on course.fast.ai still ends where it ended in 2022. Check the lesson dates yourself before you believe any round-up on this point, ours included.

“Practical” is contested by some of its own students. The sharpest write-up we found is an r/learnmachinelearning post from 28 January 2024, “What I wish I knew before starting Fast.ai”: “Despite being labeled ‘practical’ deep learning, the course focuses mostly on theory and expects you to take the initiative to cement these ideas through quizzes, kaggle comps, and experimenting with the code… this is despite being taught essentially no practical coding or libraries.” Read that as an accurate description of the deal, not a complaint to dismiss. fast.ai hands you a working model and the motivation to understand it. The repetition that turns understanding into skill is homework nobody sets and nobody marks. If you need a course that checks your work, this isn’t one, and the quality of the lectures doesn’t fix it.

Then the wrapper objection, which is the most common technical complaint. “I found it odd that they’re teaching how to use their own wrapper that nobody uses instead of actually teaching how to use PyTorch itself,” a commenter wrote in November 2025. You do reach raw PyTorch, mostly in Part 2. For most of Part 1 you learn fastai, and fastai’s API is not what your next employer runs.

There’s also a difficulty cliff, in the same 2024 thread. “After lesson 5, the math around fast.ai’s course is way too complex for me and I struggle to follow the what and why,” one reply reads. Fair warning for a course marketed on not needing university maths.

And there is no certificate. The clearest statement on record is a 2017 answer on fast.ai’s own forum — from a course member, not from staff: “there is no certificate like coursera, udacity, etc.” Nothing has changed since. Wikipedia records the same thing and adds the detail that matters: only students who attended the in-person University of San Francisco classes could obtain a certificate, and that was from USF, not fast.ai.

The same forum reply offers the standard consolation, that you should upload projects to GitHub instead, which “has a higher value for a CV / application as a certificate.” Often true. Also irrelevant to an applicant-tracking system that filters on strings. If your real goal is clearing a keyword screen, teaching quality doesn’t fix this.

Who should take Practical Deep Learning for Coders, and who should skip it

Take it if you can already write Python, you have bounced off maths-first machine learning courses, and you want to be able to train and deploy a model that works. Do Part 1, run every notebook, then go build something of your own. The foundations it teaches — transfer learning, SGD, embeddings, CNNs, data ethics — have not expired, and probably won’t.

Skip it if you cannot code yet; fast.ai is explicit that a year of programming is the floor. Skip it if you need something to put on a résumé, because there is nothing to put. Skip it if your goal is LLM or agent engineering, because a 2022 curriculum will not get you there. And skip Part 2 unless you have finished Part 1 or can already write a training loop from scratch — that is fast.ai’s bar, not ours.

Roughly: 9/10 for a working programmer who wants intuition, 4/10 for a credential-seeker, and unusable for a non-coder. Our 8.0 is the honest blend for the audience fast.ai actually built it for, marked down for age.

Alternatives

If the 2022 vintage bothers you, or you need proof of completion, the options split cleanly.

For a credential, this is the wrong shelf. Our ranking of every major AI certification by real employer value covers what actually clears screens, and the Coursera AI certificates worth paying for covers the paid tier.

For free-and-certified fundamentals, our own machine learning fundamentals course issues a Certificate of Completion and assumes less coding background than fast.ai does. The wider free AI courses hub maps everything genuinely free, and free machine learning courses is the verified-no-paywall list.

Want to see where fast.ai actually sits against the paid field? The best machine learning courses online ranks them side by side, price included.

Bottom Line

Take Practical Deep Learning for Coders if you can code and want deep learning to make sense — it’s free, it’s the best teaching in the category, and lesson 2 ends with a deployed model. Do not take it expecting a certificate or 2026 content. It is a 2022 course, and fast.ai has moved on.

FAQ

Is fast.ai Practical Deep Learning for Coders really free?

Yes, completely. There is no paywall, trial, or account requirement, and the accompanying book is free online as executable notebooks. Compute is free too: fast.ai routes you to Kaggle Notebooks and Paperspace Gradient free tiers rather than asking you to buy a GPU.

Does fast.ai give you a certificate?

No. The free online course has never carried one; only students who attended the in-person University of San Francisco classes could get a certificate, and it came from USF. Your proof is whatever you build and publish. For credential-driven job screens, pick a different course.

Should I learn PyTorch first, or take the fastai course first?

Take the fastai course first if you want results early, then go to raw PyTorch. Part 1 teaches you to train and deploy through fastai’s abstractions; Part 2 drops to Python and its standard library. Plenty of learners resent that order. It still works.

Is Practical Deep Learning for Coders still worth it in 2026?

For fundamentals, yes. Transfer learning, SGD, embeddings and CNNs haven’t changed. But Part 1 is the 2022 recording and Part 2 stops at the original Stable Diffusion, so it teaches nothing about modern LLM or agent work. Treat it as a foundation course, not a current-frontier one.

Changelog

| 2026-08-05 | Second QA + humanization pass. Every figure in the spec table re-fetched live except the 2018-archive study time: course.fast.ai, part2.html, fast.ai homepage index, Wikipedia and DataCamp’s round-up all re-confirmed, including DataCamp’s false “2024–2025 updates” claim, which is still published. Four edits. (1) The Peter Norvig endorsement was framed as evidence that fast.ai’s teaching claim is independently backed; it is a book blurb displayed on fast.ai’s own page, and the page now says so and leans on Howard’s Kaggle record instead. (2) Removed “the forums are still answered by people who took the course” — an unverified currency claim on a page whose whole argument is that fast.ai stopped updating. (3) Removed the 2024 Reddit poster’s “quit after four lectures, ~20% retention per module” and the characterisation of that thread’s replies: two live sessions have failed to reach the thread body, so the detail is gone rather than flagged. The verified quote carries the criticism, now sharpened into a judgment about what the course does and doesn’t ask of you. (4) Corrected Part 2’s stated prerequisite, which understated fast.ai’s actual bar (Part 1 complete, or SGD loop from scratch plus Kaggle plus PyTorch and fastai). Added: the 2018/2019/2022 release cadence and the February 2026 blog date behind the freshness case. | Alessandro Benigni |

Next re-verify: 2026-11-05, or immediately if fast.ai announces a re-recorded edition of Part 1. The single highest-value trigger for rewriting this page is a 2026 or 2027 course refresh — the entire freshness criticism, and two of the eight rating points, hang on the 2022 date.