Fast.ai vs Coursera for Deep Learning

By Steve HarlowAI Learning CuratorPublished May 8, 2026

We've taken (and recommended) hundreds of courses from both. Here's the honest tradeoff in 2026 — by scenario, with our specific picks.

The short answer

This comparison isn't about price (Fast.ai is free) — it's about pedagogy. Pick Fast.ai if you learn best by getting working code running first and reading theory later. Pick Coursera (Andrew Ng's Deep Learning Specialization) if you learn best by building theoretical foundations before writing code.

Both approaches work for thousands of learners. Jeremy Howard's Fast.ai famously trains a state-of-the-art image classifier in lesson 1, then explains why it works. Andrew Ng's Coursera path explains gradient descent and backpropagation before you train your first model. Many ML engineers report doing both, in either order.

Side-by-side comparison

Dimension	Fast.ai	Coursera
Pricing	Free — courses, lectures, and the fastai library are all open source. Books are sold separately	Subscription ($49-$79/mo) for specializations; financial aid available; audit free per course
Pedagogy	Top-down — start with a working model, then explain the math. "Code first, theory later"	Bottom-up — build theoretical foundations (gradient descent, activation functions) before training real models
Library	fastai (built on PyTorch) — opinionated high-level API. Good for getting results fast; some criticize it for hiding too much	Andrew Ng's courses use NumPy and TensorFlow/Keras at progressively higher abstraction levels
Math expectation	Lower — Jeremy Howard explicitly teaches "no math prereqs." You'll see linear algebra and calculus when needed, in context	Moderate — Andrew Ng walks through derivatives and matrix operations carefully but doesn't skip them
Update frequency	Major version every 2-3 years (Practical Deep Learning for Coders 2022 was the most recent significant rewrite)	Specializations versioned; the 2024 ML rewrite + ongoing Generative AI courses keep the catalog current
Best for	Working programmers who learn by doing, anyone who has bounced off math-first DL courses, learners who want to ship something in week 1	Career-switchers who want a credential, learners who want theoretical confidence, anyone targeting the "Andrew Ng path" most working ML engineers actually took

Our picks by scenario

Fast.ai

You're a working programmer who has bounced off math-first courses

Fast.ai was built specifically for this learner. Jeremy Howard's pedagogical bet is that you'll absorb the theory faster after seeing what it does — and the bet works for many learners who get stuck on Coursera's gradient-descent week and quit.

Practical Deep Learning for Coders

Free 9-lesson course training image classifiers, NLP models, tabular models, and recommendation systems. Jeremy Howard teaches the fastai library on top of PyTorch. Lesson 1 ships a working bird-vs-not-bird classifier in ~10 minutes.

Free · ~70h

Open on Fast.ai →

Coursera

You want the credential and the standard career path

Andrew Ng's Deep Learning Specialization is the resume credential most working ML engineers actually have. Hiring managers recognize it instantly. Fast.ai is widely respected but less universally credentialed.

Deep Learning Specialization

5-course specialization covering neural network foundations, hyperparameter tuning, structuring ML projects, CNN, and sequence models. The reference DL course on the internet for credentialing purposes.

$49/month subscription; audit free per course · ~120h

Open on Coursera →

Coursera

You're focused on cutting-edge LLMs and generative AI

Coursera's Generative AI with Large Language Models (DeepLearning.AI + AWS) is currently the most up-to-date treatment of transformers, RLHF, and prompt engineering at this academic depth level — and it's only 3 weeks. Fast.ai's LLM coverage exists but is less structured.

Generative AI with Large Language Models

3-week course on transformer architecture, fine-tuning, RLHF, and deployment. Co-developed with AWS — labs run against real AWS resources. Shipped 2023, continuously updated.

$49 one-time or included with Coursera Plus · ~16h

Open on Coursera →

Fast.ai

You want maximum depth at zero cost

Fast.ai's part 2 (From Foundations) goes deep into building a deep learning library from scratch — matrix multiplication, autograd, optimizers, all of it. It's free and arguably more rigorous than the equivalent Coursera material. The bar is whether you'll actually do the work without external structure.

From Deep Learning Foundations to Stable Diffusion

Part 2 of Fast.ai's curriculum. Builds a deep learning library from matrix multiplication up, then implements diffusion models from scratch. ~30-50h of dense material. Free.

Free · ~30-50h

Open on Fast.ai →

FAQ

Is Fast.ai or Coursera better for deep learning in 2026?

Fast.ai if you're a working programmer who learns by doing — Jeremy Howard's top-down approach is uniquely effective for this learner profile. Coursera if you want a credential, prefer building theory before code, or have stalled out on Fast.ai's "trust me, do this" pacing. Many engineers report taking both eventually.

Is Fast.ai really free?

Yes — courses, lectures, the fastai Python library, and the supporting forum are all free. The Practical Deep Learning for Coders book costs ~$40 but is also available free as Jupyter notebooks on GitHub. There's no upsell, no premium tier.

Does Fast.ai count for hiring?

Less universally than Coursera certificates, but yes — Jeremy Howard's reputation in the field is significant, and ML hiring managers in research-adjacent roles recognize Fast.ai positively. For corporate ML / data science roles where the recruiter screens on certifications, Coursera carries more weight.

Can I do Fast.ai without prior Python experience?

Not really — Jeremy Howard recommends "at least a year of programming experience" as the prerequisite. The course teaches deep learning, not Python. Beginners should do Python for Everybody or 100 Days of Code first, then come to Fast.ai.

Not sure where to start?

Take the 2-minute path quiz and we'll match you to the right starting point based on your background and goals.

Take the quiz →

Deep Learning

Coursera vs Udacity

Self-paced specializations vs mentor-reviewed Nanodegrees. When the human code-review loop is worth the price tag.

Deep Learning

Andrew Ng's Deep Learning vs Fast.ai

Theory-first credentialed path vs Jeremy Howard's code-first free course. Pedagogy split.

Machine Learning

Coursera vs Udemy

Andrew Ng specializations vs project-first video bootcamps. Credentials, hands-on, cost — by scenario.

About these recommendations

Picks are based on direct experience with each course, not aggregated reviews. Affiliate status doesn't influence which courses appear or how they're ranked — see how this site's recommendations actually work.

Affiliate disclosure: clicks on the buttons above use affiliate links. If you buy a course, we earn a small commission at no extra cost to you. The recommendations above reflect our actual experience with these courses and platforms.

The short answer

Side-by-side comparison

Dimension	Fast.ai	Coursera
Pricing	Free — courses, lectures, and the fastai library are all open source. Books are sold separately	Subscription ($49-$79/mo) for specializations; financial aid available; audit free per course
Pedagogy	Top-down — start with a working model, then explain the math. "Code first, theory later"	Bottom-up — build theoretical foundations (gradient descent, activation functions) before training real models
Library	fastai (built on PyTorch) — opinionated high-level API. Good for getting results fast; some criticize it for hiding too much	Andrew Ng's courses use NumPy and TensorFlow/Keras at progressively higher abstraction levels
Math expectation	Lower — Jeremy Howard explicitly teaches "no math prereqs." You'll see linear algebra and calculus when needed, in context	Moderate — Andrew Ng walks through derivatives and matrix operations carefully but doesn't skip them
Update frequency	Major version every 2-3 years (Practical Deep Learning for Coders 2022 was the most recent significant rewrite)	Specializations versioned; the 2024 ML rewrite + ongoing Generative AI courses keep the catalog current
Best for	Working programmers who learn by doing, anyone who has bounced off math-first DL courses, learners who want to ship something in week 1	Career-switchers who want a credential, learners who want theoretical confidence, anyone targeting the "Andrew Ng path" most working ML engineers actually took

Our picks by scenario

Fast.ai

You're a working programmer who has bounced off math-first courses

Practical Deep Learning for Coders

Free · ~70h

Open on Fast.ai →

Coursera

You want the credential and the standard career path

Deep Learning Specialization

$49/month subscription; audit free per course · ~120h

Open on Coursera →

Coursera

You're focused on cutting-edge LLMs and generative AI

Generative AI with Large Language Models

3-week course on transformer architecture, fine-tuning, RLHF, and deployment. Co-developed with AWS — labs run against real AWS resources. Shipped 2023, continuously updated.

$49 one-time or included with Coursera Plus · ~16h

Open on Coursera →

Fast.ai

You want maximum depth at zero cost

From Deep Learning Foundations to Stable Diffusion

Part 2 of Fast.ai's curriculum. Builds a deep learning library from matrix multiplication up, then implements diffusion models from scratch. ~30-50h of dense material. Free.

Free · ~30-50h

Open on Fast.ai →

FAQ

Is Fast.ai or Coursera better for deep learning in 2026?

Is Fast.ai really free?

Does Fast.ai count for hiring?

Can I do Fast.ai without prior Python experience?

The short answer

Side-by-side comparison

Our picks by scenario

You're a working programmer who has bounced off math-first courses

You want the credential and the standard career path

You're focused on cutting-edge LLMs and generative AI

You want maximum depth at zero cost

FAQ

Not sure where to start?

You might also like

About these recommendations

The short answer

Side-by-side comparison

Our picks by scenario

You're a working programmer who has bounced off math-first courses

You want the credential and the standard career path

You're focused on cutting-edge LLMs and generative AI

You want maximum depth at zero cost

FAQ

Not sure where to start?

You might also like

About these recommendations