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Tiny Machine Learning (TinyML)

Applied Machine Learning for Embedded IoT Devices

In this exciting new Professional Certificate program, offered by Harvard University and Google engineers, you will learn about the emerging field of TinyML, its real-world applications and the future possibilities of this transformative technology.

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About the Program:

Tiny Machine Learning (TinyML) is an emerging field at the intersection of embedded machine learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise. 

This program will emphasize hands-on experience with ML training and deployment in tiny microcontroller-based devices.The course features projects based on a TinyML program kit that includes an Arm Cortex-M4 microcontroller with onboard sensors, a camera, and a breadboard with wires—enough to unlock capabilities such as image, sound, and gesture detection. Before you know it, you’ll be implementing an entire TinyML application. 

The course will also feature real-world application case studies, guided by industry leaders, that examine the challenges facing real-world TinyML deployments. 

This first-of-its-kind program will be launching Fall 2020. Please submit your information if you would like to receive updates regarding the program or opportunities to take individual courses in the program as an auditor.

What You’ll Learn:

  • Fundamentals of ML and embedded devices
  • How to gather data for ML
  • How to train and deploy tiny ML models
  • How to program in TensorFlow Lite for Microcontrollers
  • How to optimize ML models for resource-constrained devices
  • How to conceive and design your own tinyML application 
  • Familiarity with research and development at the bleeding edge of tiny ML

Meet Your Instructors:


Untitled design (2) copyVijay Janapa Reddi 

Associate Professor, Harvard University 



Pete Warden 

Technical Lead of TensorFlow Mobile and Embedded, Google 



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Laurence Moroney 

Lead AI Advocate, Google 



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