Jeremy Howard’s Fast.ai & Data Institute Certificates
The in-person certificate courses are not free, but all of the content is available on Fast.ai as MOOCs.
What is Jeremy Howard’s Fast.ai & Data Institute Certificates?
Fast.ai is an educational platform founded by Jeremy Howard that democratizes deep learning and AI education through practical, top-down teaching methods. The platform offers free online courses (MOOCs) covering practical deep learning, machine learning, and data science topics, with an emphasis on making AI accessible to programmers without extensive mathematical backgrounds. While in-person certificate programs through the Data Institute require paid enrollment, all course materials, lectures, and resources are freely available online. Fast.ai has become renowned in the AI community for its practical, applied approach that prioritise getting hands-on experience with real datasets and problems before diving into theoretical foundations.
Key Features
Free MOOC courses covering deep learning, machine learning, and NLP with video lectures and Jupyter notebooks
Practical, top-down teaching approach that starts with applications rather than mathematical theory
Access to the fastai library, an open-source PyTorch wrapper designed to simplify deep learning implementations
Community forums and discussions for peer learning and project collaboration
Pre-trained models and datasets to accelerate learning and reduce computational barriers
Optional paid in-person certificate programs through the Data Institute for structured learning with mentorship
Pros & Cons
Advantages
- Completely free access to high-quality AI and machine learning education from industry experts
- Practical, applied curriculum that emphasizes real-world problem-solving over pure theory
- Active community support and discussion forums for collaborative learning
- Includes fastai library that significantly reduces code complexity for deep learning projects
- No prerequisites in advanced mathematics, making AI education accessible to broader audiences
Limitations
- Free courses lack formal credentials or recognise certificates (unless enrolling in paid Data Institute programs)
- Requires self-discipline and motivation for self-paced learning without structured accountability
- Computationally intensive projects may require access to GPUs or cloud resources, which can incur costs
Use Cases
Learning practical deep learning and neural networks for software developers transitioning to AI
Building and training custom machine learning models for real-world business problems
Rapid prototyping of computer vision and NLP applications using pre-trained models
Teaching AI fundamentals in academic or corporate training settings
Contributing to or using the fastai library for open-source AI projects