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479 ratings

Shawn Hymel +1 more instructor

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Introduction to Embedded Machine Learning

Edge Impulse

About this Course

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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 (209 Ratings)

Shawn Hymel

Instructor

33,164 Learners

2 Courses

Alexander Fred-Ojala

26,497 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%(1,735 ratings)

Week

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 Course4m

Instructor Introductions3m

What is Machine Learning?15m

Limitations and Ethics of Machine Learning12m

Machine Learning on Embedded Devices5m

Machine Learning Specific Hardware13m

Machine Learning Software Frameworks7m

Getting Started with Edge Impulse6m

Data Collection14m

Feature Extraction from Motion Data10m

Feature Selection in Edge Impulse4m

Machine Learning Pipeline6m

Review of Module 12m

14 readings

Syllabus5m

Required Hardware5m

Getting Help2m

Slides10m

Limitations of Machine Learning15m

Slides10m

Machine Learning on Microcontrollers5m

Slides10m

Edge Impulse CLI Installation Troubleshooting10m

What Makes a Good Dataset5m

Slides10m

Feature Selection and Extraction5m

Slides10m

5 practice exercises

Machine Learning and Limitations15m

Embedded Machine Learning15m

Data Collection15m

Feature Extraction15m

Machine Learning Overview30m

Week

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 Networks15m

Model Training in Edge Impulse7m

How to Evaluate a Model10m

Underfitting and Overfitting6m

How to Use a Model for Inference6m

Testing Inference with a Smartphone3m

How to Deploy a Trained Model to Arduino9m

Anomaly Detection8m

Industrial Embedded Machine Learning Demo4m

Module Review3m

10 readings

Neural Networks and Training5m

Slides10m

Evaluation, Underfitting, and Overfitting5m

Slides10m

Using a Model for Inference5m

Slides10m

Anomaly Detection5m

Slides10m

Project - Motion Detection2h

Slides10m

5 practice exercises

Neural Networks and Training15m

Evaluation, Underfitting, and Overfitting15m

Deploy Model to Embedded System15m

Anomaly Detection5m

Motion Classification and Anomaly Detection30m

Week

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 Classification7m

Audio Data Capture11m

Audio Feature Extraction10m

Introduction to Convolutional Neural Networks11m

Modifying the Neural Network10m

Deploy Keyword Spotting System6m

Implementation Strategies10m

Sensor Fusion3m

Conclusion2m

7 readings

Sample Rate and Bit Depth5m

Slides10m

MFCCs and CNNs5m

Slides10m

Implementation Strategies and Sensor Fusion5m

Slides10m

Project - Sound Classification2h

4 practice exercises

Audio Classification and Sampling Audio Signals10m

MFCCs and CNNs15m

Implementation Strategies15m

Audio Classification30m

Reviews

4.8

123 reviews

5 stars
81.83%
4 stars
16.28%
3 stars
1.67%
1 star
0.20%

TOP REVIEWS FROM INTRODUCTION TO EMBEDDED MACHINE LEARNING

by MKMar 31, 2021

The Course is really Helpful for me. It just gave me a good and basic idea of where to start if i want to implement machine learning in the microcontroller.

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 SAFeb 17, 2021

ML on embedded systems is an exciting and growing technology and business domain. This course is a great place to start learning about it.

by KKMar 7, 2022

Excellent course with lots of practical examples and support using different platforms. 100% recommended

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