DeepLearning.AI vs fast.ai

A detailed comparison of DeepLearning.AI and fast.ai for AI and machine learning courses, covering course catalog, ratings, pricing, and certifications.

Metric

DeepLearning.AI

fast.ai

Total Courses

Average Rating

4.5 / 5.0

4.7 / 5.0

Free Courses

100%

Certificate Available

Our Verdict

DeepLearning.AI provides structured, beginner-friendly specializations taught by Andrew Ng, while fast.ai takes a top-down, practical approach that gets you building models immediately. DeepLearning.AI is best for systematic learners who want certificates, and fast.ai is perfect for hands-on practitioners who learn by doing.

DeepLearning.AI vs fast.ai: the details

DeepLearning.AI

DeepLearning.AI, the education company founded in 2017 by AI pioneer Andrew Ng, is one of the most recognized brands in applied AI/ML training, best known for its Coursera specializations and a large library of short, hands-on courses on generative AI. Its standout differentiator is that the short courses are co-created with the companies building the models and tooling, including OpenAI, Anthropic, LangChain, and Google, so learners get practical, source-level instruction on LLMs, RAG, embeddings, vector databases, and agents. The short courses on the DeepLearning.AI platform are free, and the company is explicit that, at present, they carry no certificate; credential-bearing assessments and certificates come via its paid Coursera programs or the DeepLearning.AI Pro subscription. It is an excellent first stop for practitioners who want to build with current AI tools quickly, with the caveat that the bite-sized format favors breadth and momentum over deep, exam-backed credentials.

Best for: Developers, data scientists, and technically comfortable learners who want fast, practical, hands-on instruction on the current generative-AI stack (prompt engineering, LangChain, RAG, embeddings, vector databases, and agents) directly from the teams that build the models, and who value building real projects over collecting credentials.

Pricing: Freemium with a paid subscription and per-program options. The short courses on the DeepLearning.AI platform are free (free during the learning-platform beta, per the official FAQ) but currently come with no certificate. Certificate-bearing learning runs through either the DeepLearning.AI Pro subscription (a paid monthly/annual membership that unlocks graded assessments and certificates; widely reported around $30/month billed monthly or about $25/month billed annually, though the live membership page should be checked for the current figure) or Coursera, where programs offer a free 'Full Course, No Certificate' audit track and a paid certificate track. Coursera financial aid is available to learners who cannot afford the fee.

Strengths

Short courses are co-created with the organizations building the models and tooling (OpenAI, Anthropic, LangChain, Google), giving learners practical, source-level instruction rather than second-hand summaries.
Strong brand credibility: the DeepLearning.AI name and Andrew Ng's association are widely recognized by recruiters and hiring managers, which adds real signal on a resume and LinkedIn profile.
Genuinely free, low-friction access to short courses (no credit card or trial required during the platform beta), with interactive Jupyter notebooks for hands-on practice.
Consistently high learner satisfaction on its flagship Coursera programs (for example, AI For Everyone holds roughly a 4.8 rating across tens of thousands of reviews, and the Deep Learning Specialization has 147,000+ reviews).

Weaknesses

The short courses currently issue no certificate of completion, so they do not function as standalone credentials; learners must use the paid Coursera programs or the Pro subscription to earn certificates.
The 1-2 hour short-course format favors breadth and momentum over depth, with thin coverage of production deployment, cost optimization, evaluation, and multi-agent systems.
Because content is structured for self-motivated learners, it is easy to passively watch courses back-to-back and build nothing; the format demands self-discipline to convert lessons into projects.

Full DeepLearning.AI review →

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.

Full fast.ai review →