https://github.com/afondiel/edge-ai-courses
A curated collection of Edge AI courses for everyone
https://github.com/afondiel/edge-ai-courses
edge-ai edge-ai-courses edge-ai-engineering embedded-ai frugal-ai
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A curated collection of Edge AI courses for everyone
- Host: GitHub
- URL: https://github.com/afondiel/edge-ai-courses
- Owner: afondiel
- License: mit
- Created: 2025-06-07T16:37:25.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-07-19T21:18:16.000Z (4 months ago)
- Last Synced: 2025-08-22T01:48:04.817Z (2 months ago)
- Topics: edge-ai, edge-ai-courses, edge-ai-engineering, embedded-ai, frugal-ai
- Homepage:
- Size: 19.5 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
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README
[](./CONTRIBUTING.md)
# Edge AI Courses
This repository offers a curated collection of Edge AI courses and resources for engineers and practitioners of all levels.
### New to Edge AI?
- Start with [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering): a practical guide covering core concepts of the entire [Edge AI MLOps](https://docs.edgeimpulse.com/docs/concepts/edge-ai-fundamentals/what-is-edge-mlops) stack with industry blueprints.
- Next, read ["The Next AI Frontier is at the Edge"](https://afondiel.github.io/posts/the-next-ai-frontier-is-at-the-edge/) to discover Edge AI’s benefits and its rapidly evolving landscape.
- Edge AI is all about solving real-world problems. Get started with these practical, real-world **edge-series**: [Edge Language (Coming Soon!)](https://github.com/afondiel/edge-audio), [Edge Audio](https://github.com/afondiel/edge-audio), [Edge Vision](https://github.com/afondiel/edge-vision)
## Courses
| Course | Description | Level | Price | Organization |
| :------------------------------------------------- | :------------------------------------------------------------------------------------------------------- | :--------- | :------------------------------ | :------------------------------------------------------------------------------------------------------- |
| AIoT Foundations | Learn the full lifecycle of AIoT product development, from use cases to system operation. | Beginner | Paid (cert) | [Udacity](https://www.udacity.com/course/aiot-foundations--ud074) |
| Fundamentals of Qualcomm AI | Covers fundamentals of AI on Qualcomm platforms, focusing on practical implementation. | Beginner | **Free** | [Qualcomm / GitHub (afondiel)](https://github.com/afondiel/Fundamentals-of-Qualcomm-AI) |
| Introduction to On-Device AI | Gain skills to deploy AI on devices, covering model conversion, quantization, and hardware acceleration. | Beginner | **Free** | [Qualcomm / DeepLearning.AI](https://www.deeplearning.ai/short-courses/introduction-to-on-device-ai/) |
| Getting Started with AI on Jetson Nano | Introduces AI development on NVIDIA Jetson Nano, focusing on computer vision and practical projects. | Beginner | **Free** | [NVIDIA DLI](https://learn.nvidia.com/courses/course-detail?course_id=course-v1:DLI+S-RX-02+V2) |
| Introduction to Embedded ML | A gentle intro to TinyML, deploying models to microcontrollers, and using Edge Impulse. | Beginner | **Free** (audit) or Paid (cert) | [Edge Impulse / Coursera](https://www.coursera.org/learn/introduction-to-embedded-machine-learning) |
| Device-based Models with TensorFlow Lite | Learn to execute ML models on battery-operated devices (Android, iOS, Raspberry Pi, microcontrollers). | Intermediate | Paid (for certificate) | [DeepLearning.AI / Coursera](https://www.coursera.org/learn/device-based-models-tensorflow/) |
| ESE3600 tinyML | Intro to ML and Embedded IoT Devices; covers ML applications on embedded hardware. | Intermediate | Course specific, part of UPenn curriculum | [University of Pennsylvania (UPenn)](https://tinyml.seas.upenn.edu/) |
| TensorFlow Data and Deployment Specialization | Learn to navigate ML model deployment scenarios and effectively use data for training. | Intermediate | Paid (subscription or course) | [Google / DeepLearning.AI](https://www.coursera.org/specializations/tensorflow-data-and-deployment) |
| Intel Edge-AI Certification | Hands-on training with Intel edge AI tools (OpenVINO toolkit, DevCloud) for certification. | Intermediate | Paid (for certification assessment) | [Intel](https://www.intel.com/content/www/us/en/support/articles/000090160/programs/intel-ai-builders.html) |
| Hello AI World | A project for real-time AI inference on NVIDIA Jetson, with examples for computer vision models. | Intermediate | **Free** | [NVIDIA / GitHub (dusty-nv)](https://github.com/dusty-nv/jetson-inference#readme) |
| CS249r tinyML | In-depth course on TinyML applications, algorithms, hardware, and software. | Advanced | Course specific, part of Harvard curriculum | [Harvard](https://sites.google.com/g.harvard.edu/tinyml/home) |
| Intel Edge-AI for IoT Developers (Nanodegree-nd131) | Develop high-performance computer vision and deep learning apps for edge devices using OpenVINO. | Advanced | (Nanodegree price) | [Udacity](https://www.udacity.com/course/intel-edge-ai-for-iot-developers-nanodegree--nd131) |
| MIT 6.5940 Efficient ML and TinyML | Focuses on efficient ML, covering model compression, quantization, and on-device fine-tuning. | Advanced | **Free** (part of MIT curriculum) | [MIT HAN Lab](https://hanlab.mit.edu/courses/2024-fall-65940) |
| Edge-ai and edge-cv | Generic Udemy course on Edge AI/CV. | Varied | Varied (often discounted) | [Udemy](https://www.udemy.com/) |
## Contributing
If you are aware of an exceptional Edge AI course or resource not yet listed here, or have suggestions for enhancing this repository, please feel free to open an [issue](https://github.com/afondiel/edge-ai-courses/issues) or submit a [pull request](https://github.com/afondiel/edge-ai-courses/pulls).
For detailed guidelines on how to contribute effectively, please consult our [contribution guide](https://github.com/afondiel/edge-ai-courses/blob/main/CONTRIBUTING.md).
Let's make [Edge AI Engineering](https://github.com/afondiel/edge-ai-engineering) accessible to everyone! 🚀