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.
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Introduction to Embedded Machine Learning
Edge ImpulseAbout this Course
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Start instantly and learn at your own schedule.
Flexible deadlines
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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. 15 hours to complete
English
Subtitles: English
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
ArduinoMachine LearningEmbedded System DesignMicrocontrollerComputer Programming
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your 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. 15 hours to complete
English
Subtitles: English
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.
Syllabus - What you will learn from this course
4 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).
4 hours to complete
13 videos (Total 107 min), 8 readings, 5 quizzes
5 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.
5 hours to complete
10 videos (Total 77 min), 5 readings, 5 quizzes
5 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.
5 hours to complete
9 videos (Total 75 min), 4 readings, 4 quizzes
Reviews
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 RSMar 3, 2021
Very good arrange of topics and explain complex topics as simply as possible. Recommended course for anyone who needs to start in embedded machine learning.
by TNMar 14, 2021
The videos and supplemental materials were well-presented and very useful. The hands-on projects were the best for learning practical use of the concepts.
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.
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