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Fundamentals of TinyML

Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Fundamentals of TinyML

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Estimated 5 weeks
2–4 hours per week
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About this course

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What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.

TinyML is 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.

The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.

Following Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used Tiny ML applications—such as tiny devices and smartphones—and deploy code to your own physical TinyML device. Fundamentals of TinyML provides an introduction to TinyML and is not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.

At a glance

What you'll learn

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  • Fundamentals of Machine Learning (ML)
  • Fundamentals of Deep Learning
  • How to gather data for ML
  • How to train and deploy ML models
  • Understanding embedded ML
  • Responsible AI Design
  • Chapter 1: Welcome to TinyML
  • Chapter 1.1: Course Overview
  • Chapter 1.2: The Future of ML is Tiny and Bright
  • Chapter 1.3: TinyML Challenges
  • Chapter 1.4: Getting Started

  • Chapter 2: Introduction to (Tiny) ML

  • Chapter 2.1: The Machine Learning Paradigm
  • Chapter 2.2: The Building Blocks of Deep Learning
  • Chapter 2.3: Exploring Machine Learning Scenarios
  • Chapter 2.4: Building a Computer Vision Model
  • Chapter 2.5: Responsible AI Design
  • Chapter 2.6: Summary

About the instructors

Who can take this course?

Unfortunately, learners from one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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