Tiny Machine Learning (TinyML) by Harvard

Skills Covered: Tiny Machine Learning, TinyML Applications, TinyML Implementation, TinyML Challenges, Machine Learning Paradigm, Building Blocks of Deep Learning, Machine Learning Scenarios, Computer Vision Model, AI Design, AI Lifecycle, ML Workflow, Visual Wake Words, Anomaly Detection, Dataset Engineering, Keyword Spotting, TinyML Kit, TinyML Dataset,  TinyML Optimization, TensorFlow Programming, TensorFlow Lite for Microcontrollers

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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.



  • 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



  • There are hundreds of billions of microcontrollers today, and an increasing desire to deploy machine learning models on these devices through TinyML. Learners who complete this program will be prepared to dive into this fast-growing field.
  • Learners will have a fundamental understanding of TinyML applications and use cases and gain hands-on experience in programming with TensorFlow Micro and deploying TinyML models to an embedded microprocessor and system.

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Tiny Machine Learning (TinyML) by Harvard
Tiny Machine Learning (TinyML) by Harvard


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