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Independent verification for online courses.

Artificial IntelligenceSelf-pacedIntermediate

Practical Deep Learning for Coders

by fast.aiTaught by Jeremy Howard

Verification record

Verification identifier TMC-2026-00014

Verifiedby TrustMyCourse
Method
Direct course review
Verified
Re-check due
Price
Free
Level
Intermediate
Format
Self-paced
Language
English
Modules
9

What we found

TrustMyCourse opened the published course directly. Lesson one delivered its video, the accompanying notebooks and the free online book chapter, and the advertised lesson sequence matched what is served. The lessons and book are free to read, but the practical notebooks run on Kaggle, which requires a separate free account. That is worth knowing before starting. No outcome or employment guarantee is made.

View Practical Deep Learning for Coders on fast.ai, opens the provider’s website

on course.fast.ai

You will continue to fast.ai. TrustMyCourse does not sell this course.

See what was checked
  • Curriculum matches the offerChecked
  • Creator identityChecked
  • Access and deliveryChecked
  • Advertised claimsChecked
  • Pricing and termsChecked
  • Support and updatesChecked

TrustMyCourse accessed the course directly and compared the delivered material with its public offer.

Reviewer access: Confirmed

Read what each check covers

A free deep learning course that starts by training and deploying a working model in the first lesson and only then works backwards into the theory. It covers computer vision, natural language processing, tabular data with random forests, collaborative filtering and building a model from scratch, taught in PyTorch and the fastai library through Jupyter notebooks. It assumes about a year of coding experience, preferably Python, and no mathematics beyond school level. The edition published at the time of checking is labelled 2022.

Who it is for

Programmers with roughly a year of coding experience who want to build working deep learning models without a research background.

Recorded from the provider’s stated audience and the level of the material reviewed.

The review

What was checked

Verified against the course and public offer as they stood on . Verification describes what was reviewed on that date, not every later version of it.

Read the verification standards

  • Curriculum matches the offer

    Checked

    The modules, lessons and materials advertised on the sales page are the ones actually delivered inside the course.

    Advertised lesson sequence matched what is served; lesson one inspected end to end.

  • Creator identity

    Checked

    The person or company selling the course is identifiable and confirmed to control the course being verified.

    Published on the provider’s own domain.

  • Access and delivery

    Checked

    The stated platform, access method and access duration work as described for a paying learner.

    Lessons and book chapters are free to read with no account. The practical notebooks run on Kaggle, which requires a separate free account, and this is disclosed in the public summary.

  • Advertised claims

    Checked

    Public marketing is checked for outcome guarantees the course cannot support, such as promised income, results or employment.

  • Pricing and terms

    Checked

    The advertised price, billing terms, additional purchases and refund policy are stated clearly and consistently.

    Free; the printed book is a separate optional purchase.

  • Support and updates

    Checked

    Any promised feedback, community access or update schedule is described accurately on the sales page.

    The course links its public forums from the lesson pages, which matches the support described on the record. No response time or level of assistance is promised, and none is implied here.

What this verification does not guarantee

  • Whether you will earn money, find work or reach any particular result
  • Whether a course is the right choice for your situation
  • Teaching quality, or the personal opinions of previous buyers
  • Anything a creator changes between scheduled re-checks

TrustMyCourse does not sell this course, does not rank it against others, and publishes no rating or score for it.

The course

Course details

Provider
fast.ai
Price
Free
Level
Intermediate
Format
Self-paced
Language
English
Access
Free access to video lessons, notebooks and the accompanying book online

Listed in

What the course covers

Summarised by TrustMyCourse from the material reviewed. Wording is our own, not the provider’s.

  1. Getting started

    Training a usable image model in the first session, before any of the underlying theory.

  2. Deployment

    Putting a trained model somewhere other people can use it, early rather than at the end.

  3. Neural network foundations

    What the training loop is actually doing, revisited once a model has already been built.

  4. Natural language processing

    Applying the same approach to text, including working with pretrained transformer models.

  5. Building a model from scratch

    Reconstructing the pieces the library normally provides, to show what it is abstracting.

  6. Random forests and tabular data

    Tree-based methods for structured data, where deep learning is not automatically the right tool.

  7. Collaborative filtering

    Recommendation problems and the embedding ideas behind them.

  8. Convolutions and CNNs

    The architecture behind the vision results from the opening lessons.

  9. Data ethics

    A bonus unit on the consequences of deploying models, included alongside the technical lessons.

Provider

Who created this course

fast.ai

Taught by Jeremy Howard

A research group that publishes free deep learning courses and maintains the fastai open-source library.

fast.ai website

Any TrustMyCourse verification applies to the course on this page, not to the provider as a whole and not to its other courses. TrustMyCourse does not endorse fast.ai.

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