Applications of TinyML

Experience TinyML in practice

This online course helps you learn first-hand how to train models for tiny applications such as keyword spotting, visual wake words, and gesture recognition.

Featuring faculty from:
Self-Paced
Length
6 weeks
2-4 hours per week
Certificate Price
$299
Program Dates
Start Applications of TinyML today.

What You'll Learn

Do you know what happens when you say “OK Google” to a Google device? Is your Google Home always listening?

Following on the Foundations of Tiny ML course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.

Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind “OK Google,” “Alexa,” and smartphone features on Android and Apple . Learn about real-word industry applications of TinyML as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.

Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in the TinyML Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.

The course will be delivered via edX and connect learners around the world. By the end of the course, participants will learn:

  • The code behind some of the most widely used applications of TinyML
  • Real-word industry applications of TinyML
  • Principles of Keyword Spotting
  • Principles of Visual Wake Words
  • Concept of Anomaly Detection
  • Principles of Dataset Engineering
  • Responsible AI Development

Course Overview

  • Chapter 1.1: Welcome to Applications of TinyML
  • Chapter 1.2: AI Lifecycle and ML Workflow
  • Chapter 1.3: Machine Learning on Mobile and Edge IoT Devices - Part 1
  • Chapter 1.4: Machine Learning on Mobile and Edge IoT Devices - Part 2
  • Chapter 1.5: Keyword Spotting
  • Chapter 1.6: Data Engineering for TinyML Applications
  • Chapter 1.7: Visual Wake Words
  • Chapter 1.8: Anomaly Detection
  • Chapter 1.9: Responsible AI Development
  • Chapter 1.10: Summary

Your Instructors

Image
Vijay Janapa Reddi Headshot

Vijay Janapa Reddi

Associate Professor at John A. Paulson,
School of Engineering and Applied Sciences (SEAS),
at Harvard University
Read full bio.

Image
Laurence Moroney Headshot

Laurence Moroney

Lead AI Advocate
at Google
Read full bio.

Ways to take this course

When you enroll in this course, you will have the option of pursuing a Verified Certificate or Auditing the Course.

A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate. 

Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.

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