fast.ai vs Kaggle
A detailed comparison of fast.ai and Kaggle for AI and machine learning courses, covering course catalog, ratings, pricing, and certifications.
Our Verdict
fast.ai teaches deep learning through a structured, code-first curriculum that builds comprehensive understanding, while Kaggle develops skills through competitive challenges on real-world datasets. fast.ai provides a better learning foundation for deep learning concepts, and Kaggle is superior for building a public track record and practicing on diverse data problems.
fast.ai vs Kaggle: the details
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.
Kaggle
Kaggle (a Google subsidiary) runs Kaggle Learn, a set of free, browser-based micro-courses that teach practical data science and machine learning in roughly 1-7 hours each. The format is deliberately hands-on: short concept explanations followed by interactive Jupyter notebook exercises with hints and solutions, using Python, pandas, scikit-learn, TensorFlow/Keras, Seaborn, and BigQuery SQL. Independent reviewers consistently praise the courses as an accessible, fast-track way to learn fundamentals or refresh skills, while noting they are intentionally light on theory and will not, on their own, make you an expert. Completion certificates are free and shareable, but employers regard them as a weak standalone signal compared with Kaggle competition results and real projects.
Best for: Beginners and working developers who want fast, practical, hands-on fundamentals in Python, pandas, machine learning, deep learning, and SQL without paying anything, plus people who want a low-friction on-ramp into Kaggle competitions and notebooks.
Pricing: Free. All Kaggle Learn micro-courses are available at no cost with no subscription, per-course fee, or audit restriction, and free completion certificates are issued.
Strengths
- Completely free with no paywall, audit limits, or financial-aid gatekeeping; the catalog of around a dozen-plus micro-courses costs nothing
- Strongly hands-on format where every lesson runs in an in-browser Jupyter notebook with exercises, hints, and worked solutions, so you write and run code immediately
- Short, modular structure (each course roughly 1-7 hours over a few lessons) that lets learners finish in a sitting and avoid the drop-off common in long programs
- Practical, industry-standard tooling taught in context (pandas, scikit-learn, TensorFlow/Keras, Seaborn, Google BigQuery SQL) rather than abstract theory
Weaknesses
- Intentionally shallow on theory and math; reviewers note the courses give a solid foundation but will not make you an expert data scientist on their own
- Certificates are downloadable and shareable but carry limited hiring value, learners and recruiters repeatedly emphasize that projects and competition results matter far more than the completion badges
- No instructor support, mentorship, graded feedback, or cohort structure, the courses are fully self-paced and self-checked
Top Courses
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
