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502 ratings

Shawn Hymel +1 more instructor

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Introduction to Embedded Machine Learning

Edge Impulse

About this Course

53,809 recent views

Machine learning (ML) allows us to teach computers to make predictions and decisions based on data and learn from experiences. In recent years, incredible optimizations have been made to machine learning algorithms, software frameworks, and embedded hardware. Thanks to this, running deep neural networks and other complex machine learning algorithms is possible on low-power devices like microcontrollers.
This course will give you a broad overview of how machine learning works, how to train neural networks, and how to deploy those networks to microcontrollers, which is known as embedded machine learning or TinyML. You do not need any prior machine learning knowledge to take this course. Familiarity with Arduino and microcontrollers is advised to understand some topics as well as to tackle the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects.

We will cover the concepts and vocabulary necessary to understand the fundamentals of machine learning as well as provide demonstrations and projects to give you hands-on experience.

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).

Approx. 17 hours to complete

English

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What you will learn

The basics of a machine learning system
How to deploy a machine learning model to a microcontroller
How to use machine learning to make decisions and predictions in an embedded system

Skills you will gain

Arduino
Machine Learning
Embedded System Design
Microcontroller
Computer Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Intermediate Level

Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).

Approx. 17 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructors

Instructor rating

4.83/5 (218 Ratings)

Shawn Hymel

Instructor

36,096 Learners

2 Courses

Alexander Fred-Ojala

28,808 Learners

1 Course

Offered by

Edge Impulse

Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises. Founded in 2019 by Zach Shelby and Jan Jongboom, we are on a mission to enable developers to create the next generation of intelligent devices. We believe that machine learning can enable positive change in society, and we are dedicated to support applications for good.

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Syllabus - What you will learn from this course

Content Rating96%(1,798 ratings)

Week1

Week 1

6 hours to complete

Introduction to Machine Learning

In this module, we will introduce the concept of machine learning, how it can be used to solve problems, and its limitations. We will also cover how machine learning on embedded systems, such as single board computers and microcontrollers, can be effectively used to solve problems and create new types of computer interfaces. Then, we will introduce the Edge Impulse tool and collect motion data for a "magic wand" demo. Finally, we will examine the various features that can be calculated from this raw motion data, including root mean square (RMS), Fourier transform, and power spectral density (PSD).

6 hours to complete

13 videos (Total 107 min), 14 readings, 5 quizzes

13 videos

Welcome to the Course4mInstructor Introductions3mWhat is Machine Learning?15mLimitations and Ethics of Machine Learning12mMachine Learning on Embedded Devices5mMachine Learning Specific Hardware13mMachine Learning Software Frameworks7mGetting Started with Edge Impulse6mData Collection14mFeature Extraction from Motion Data10mFeature Selection in Edge Impulse4mMachine Learning Pipeline6mReview of Module 12m

14 readings

Syllabus5mRequired Hardware5mGetting Help2mSlides10mLimitations of Machine Learning15mSlides10mMachine Learning on Microcontrollers5mSlides10mEdge Impulse CLI Installation Troubleshooting10mWhat Makes a Good Dataset5mSlides10mFeature Selection and Extraction5mSlides10mSlides10m

5 practice exercises

Machine Learning and Limitations15mEmbedded Machine Learning15mData Collection15mFeature Extraction15mMachine Learning Overview30m

Week2

Week 2

6 hours to complete

Introduction to Neural Networks

In this module, we will look at how neural networks work, how to train them, and how to use them to perform inference in an embedded system. We will continue the previous demo of creating a motion classification system using motion data collected from a smartphone or Arduino board. Finally, we will challenge you with a new motion classification project where you will have the opportunity to implement the concepts learning in this module and the previous module.

6 hours to complete

10 videos (Total 77 min), 10 readings, 5 quizzes

10 videos

Introduction to Neural Networks15mModel Training in Edge Impulse7mHow to Evaluate a Model10mUnderfitting and Overfitting6mHow to Use a Model for Inference6mTesting Inference with a Smartphone3mHow to Deploy a Trained Model to Arduino9mAnomaly Detection8mIndustrial Embedded Machine Learning Demo4mModule Review3m

10 readings

Neural Networks and Training5mSlides10mEvaluation, Underfitting, and Overfitting5mSlides10mUsing a Model for Inference5mSlides10mAnomaly Detection5mSlides10mProject - Motion Detection2hSlides10m

5 practice exercises

Neural Networks and Training15mEvaluation, Underfitting, and Overfitting15mDeploy Model to Embedded System15mAnomaly Detection5mMotion Classification and Anomaly Detection30m

Week3

Week 3

6 hours to complete

Audio classification and Keyword Spotting

In this module, we cover audio classification on embedded systems. Specifically, we will go over the basics of extracting mel-frequency cepstral coefficients (MFCCs) as features from recorded audio, training a convolutional neural network (CNN) and deploying that neural network to a microcontroller. Additionally, we dive into some of the implementation strategies on embedded systems and talk about how machine learning compares to sensor fusion.

6 hours to complete

9 videos (Total 75 min), 7 readings, 4 quizzes

9 videos

Introduction to Audio Classification7mAudio Data Capture11mAudio Feature Extraction10mIntroduction to Convolutional Neural Networks11mModifying the Neural Network10mDeploy Keyword Spotting System6mImplementation Strategies10mSensor Fusion3mConclusion2m

7 readings

Sample Rate and Bit Depth5mSlides10mMFCCs and CNNs5mSlides10mImplementation Strategies and Sensor Fusion5mSlides10mProject - Sound Classification2h

4 practice exercises

Audio Classification and Sampling Audio Signals10mMFCCs and CNNs15mImplementation Strategies15mAudio Classification30m

Reviews

4.8

129 reviews

5 stars
82.41%
4 stars
15.81%
3 stars
1.58%
1 star
0.19%

TOP REVIEWS FROM INTRODUCTION TO EMBEDDED MACHINE LEARNING

by ELFeb 27, 2021

A great introduction to tinyML and embedded machine learning using Edge Impulse to get started working on projects immediately.

by MZJul 20, 2021

Best courser, as we are not just learning about the Embedded ML, as we also learn the fundamentals of ML.

by SJAug 18, 2021

Awesome course for beginners. I don't know how much of my background helped make this awesome, but it is awesome.

by EAAug 10, 2021

As everybody says they go at a really fast pace, I had to watch like three times each video, but the content is really good and concise. Thanks to the sponsors and to the teacher

View all reviews

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I purchase the Certificate?
When you purchase a Certificate you get access to all course materials, including graded assignments. Upon completing the course, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.
Do I need to buy hardware to take this course?
No hardware is required to complete the course. However, we recommend purchasing an Arduino Nano 33 BLE Sense in order to do the optional projects. Links to sites that sell the board will be provided in the course.
What prior knowledge do I need?
We recommend having some experience with embedded systems (such as programming an Arduino board or other microcontroller) and familiarity with the C/C++ language(s). No prior machine learning knowledge is required (but if you do have some, this course might be a good review). You will be required to use some math (reading plots, arithmetic, and algebra) to complete the quizzes and projects.

More questions? Visit the Learner Help Center.

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Introduction to Embedded Machine Learning

Introduction to Embedded Machine Learning

About this Course

What you will learn

Skills you will gain