What you'll learn

  • Fundamentals of machine learning and embedded devices.
  • How to gather data effectively for machine learning.
  • How to train and deploy tiny machine learning models.
  • How to optimize machine learning models for resource-constrained devices.
  • How to conceive and design your own tiny machine learning application.
  • How to program in TensorFlow Lite for Microcontrollers, using an ARM Cortex-M4

Course description

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

TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software.

The program will emphasize hands-on experience with training and deploying machine learning into tiny embedded devices. This series of courses features projects based on a TinyML Program Kit that includes an Arduino board with onboard sensors and an ARM Cortex-M4 microcontroller. To ensure you hit the road running, the kit also comes equipped with a camera. The TinyML Program Kit has everything you need to unlock your imagination and build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application.

This first-of-its-kind program combines computer science with engineering to feature real-world application case studies that examine the challenges facing TinyML deployments.

This program is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team.


  • Associate Professor at John A. Paulson School of Engineering and Applied Sciences (SEAS), Harvard University
  • Technical Lead of TensorFlow Mobile and Embedded at Google
  • Lead AI Advocate at Google

Associated Schools

  • Harvard School of Engineering and Applied Sciences

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