Program profile · official sources
Practical Deep Learning for Coders
Practical Deep Learning for Coders is fast.ai's free online course for people with some coding experience who want to apply deep learning and machine learning to practical problems. Part 1, recorded in 2022 at the University of Queensland and taught by Jeremy Howard, has 9 lessons of around 90 minutes on computer vision, natural language processing, tabular data, collaborative filtering, random forests and deployment, using PyTorch, fastai, Hugging Face Transformers and Gradio. The only stated prerequisite is about a year of coding, preferably in Python, plus high school math. A separate part 2 runs more than 30 hours. No certificate is mentioned on the page. As shown in the United States on 7 October 2026, the course is free.
Facts checked 7 Oct 2026
- Price
- Free
- (As shown in the United States on 7 October 2026, the official course page calls it "A free course designed for people with some coding experience" and says the course book is freely available online.)
- Provider link
See price and enroll at fast.ai (official site) ↗
Official site. No commission.
- Time
- 9 lessons of around 90 minutes each (part 1, provider-listed); part 2 is a separate video course of more than 30 hours
- Level
- Not listed
- Credential
- Not listed
- Prerequisites
- The page states that the only prerequisite is knowing how to code (a year of experience is enough), preferably in Python, and having at least followed a high school math course; it says no special hardware or software and no university math are needed.
- Format
- Free online video course (part 1, recorded in 2022 at the University of Queensland), with captioned videos, the course book freely available online and its chapters as Jupyter notebooks
Commercial status: no commercial relationship is documented for this page. The verification link goes directly to the official provider source.
Sources ↗
This program is free. It is not plotted on the logarithmic price scale. See the sourced pricing details below.
See the full registry price landscape. Prices retain their stated payment scope; they are not comparable completion costs.
What’s covered
- Getting started
- Deployment
- Neural net foundations
- Natural Language (NLP)
- From-scratch model
- Random forests
- Collaborative filtering
- Convolutions (CNNs)
- Data ethics
Prerequisites: The page states that the only prerequisite is knowing how to code (a year of experience is enough), preferably in Python, and having at least followed a high school math course; it says no special hardware or software and no university math are needed.
What you actually do
- Getting started : Part 1, lesson 1 (navigation title); lessons are around 90 minutes each per the provider · Source
- Deployment : Part 1, lesson 2 (navigation title); lessons are around 90 minutes each per the provider · Source
- Neural net foundations : Part 1, lesson 3 (navigation title); lessons are around 90 minutes each per the provider · Source
- Natural Language (NLP) : Part 1, lesson 4 (navigation title); lessons are around 90 minutes each per the provider · Source
- From-scratch model : Part 1, lesson 5 (navigation title); lessons are around 90 minutes each per the provider · Source
- Random forests : Part 1, lesson 6 (navigation title); lessons are around 90 minutes each per the provider · Source
- Collaborative filtering : Part 1, lesson 7 (navigation title); lessons are around 90 minutes each per the provider · Source
- Convolutions (CNNs) : Part 1, lesson 8 (navigation title); lessons are around 90 minutes each per the provider · Source
- Data ethics : Part 1 bonus lesson (navigation title) · Source
Total cost to finish
Cost for the stated payment scope: Free (0).
- Course access: 0 × 1 = 0. The official page describes the course as free. · Source
Scope: The page calls the course free and says the book is freely available. For computing it recommends Kaggle Notebooks and Paperspace Gradient, which it says have good free options, and strongly suggests not training models on your own computer unless you are very experienced with Linux system administration and GPU drivers. · Source
Alternatives in the registry
Related by topic or level.
- Deep Learning Specialization : Check official price; Overview: 3 months at 10 hours a week. FAQ: at 5 hours a week, each course typically takes 5 weeks except course 3, which takes about 4 weeks. These are separate provider estimates; no combined completion promise is inferred..
- Natural Language Processing Specialization : Check official price; Overview: 3 months at 10 hours a week. FAQ: at 5 hours a week, each course typically takes 4 weeks. These are separate provider estimates; no combined completion promise is inferred..
- TensorFlow: Advanced Techniques Specialization : Check official price; 2 months to complete at 10 hours a week. FAQ notes 3-4 weeks to complete each course at 5 hours a week..
Change log
- : Initial record from the official course page captured from the United States on 7 October 2026. · Source
See price and enroll at fast.ai (official site) ↗
Official site. No commission.