University of Helsinki vs fast.ai
A detailed comparison of University of Helsinki and fast.ai for AI and machine learning courses, covering course catalog, ratings, pricing, and certifications.
Our Verdict
University of Helsinki's Elements of AI is an excellent, free introduction to AI concepts for complete beginners, while fast.ai dives straight into practical deep learning for those with some coding ability. Start with Helsinki for foundational AI literacy, then graduate to fast.ai when you are ready to build and train models.
University of Helsinki vs fast.ai: the details
University of Helsinki
The University of Helsinki, Finland's oldest and largest university, is best known in AI education for Elements of AI, a free, non-technical introduction it built with MinnaLearn (and Reaktor) as part of a Finnish national upskilling initiative. The program has drawn over 2 million learners across more than 170 countries and is delivered as self-paced MOOCs combining reading with interactive, auto- and peer-graded exercises rather than video lectures. Its catalog here centers on AI literacy and ethics (Elements of AI and Ethics of AI) rather than deep, hands-on ML engineering, and completers receive an official University of Helsinki certificate with the option of free ECTS credits through the Open University. It is one of the most reputable free entry points to understanding what AI can and cannot do.
Best for: Non-technical beginners, students, and working professionals (in product, design, marketing, management, policy, or any field) who want a credible, math-free, university-backed grounding in what AI is, how machine learning and neural networks work conceptually, and the ethical and societal implications of AI, at zero cost and at their own pace.
Pricing: Free. All courses (Introduction to AI, Building AI, Ethics of AI) are completely free to access online and self-paced. An official University of Helsinki certificate is included at no cost, and ECTS credits (up to 8 across the AI collection) can also be obtained free by enrolling through the Open University.
Strengths
- Genuinely free with a credible credential: an official certificate from the University of Helsinki, plus up to 8 ECTS credits across the AI collection (e.g. 2 ECTS for Introduction to AI) via the Open University, with no audit/paywall trade-off.
- Exceptional accessibility for non-experts: Introduction to AI requires no math or programming, uses plain language and relatable examples, and is self-paced with saved progress.
- Unusually strong ethics and societal-impact coverage: the dedicated Ethics of AI course (seven chapters on bias, privacy, accountability, responsibility) goes well beyond what most free AI courses offer.
- Proven scale and reputation: 2M+ enrollments across 170+ countries, roughly 40% women, broadly positive learner sentiment on Class Central and review sites, and design recognition (German Design Award 2022).
Weaknesses
- Conceptual rather than applied: heavy reading, no video, and limited hands-on coding mean it builds understanding and literacy more than practical, employable engineering skills.
- Certificate is respected as proof of AI literacy but is not a heavyweight, career-gating credential; employer recognition is modest compared with vendor or specialized certificates, and it is best framed as foundational on a LinkedIn profile.
- ECTS credit availability and the free-credit path are tied to enrolling through the University of Helsinki Open University, which can be confusing and is most straightforward for learners in Finland/the EU.
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.
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.
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.
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.
Top Courses
Top from University of Helsinki
Top from fast.ai
Practical Deep Learning for Coders
fast.ai
Practical Deep Learning for Coders Part 2: Deep Learning Foundations to Stable Diffusion
fast.ai
Computational Linear Algebra
fast.ai