Fundamentals of TinyML

Learn the language of TinyML

This online course focuses on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the language of TinyML.

Featuring faculty from:
Self-Paced
Length
5 weeks
2-3 hours per week
Certificate Price
$299
Program Dates
Start Fundamentals of TinyML today.

What You'll Learn

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.

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

  • 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

Course Overview

  • 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

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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