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.
Introduction to Embedded Machine Learning
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Introduction to Embedded Machine Learning
Instructors: Shawn Hymel
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4.8
(671 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 17 hours
Learn at your own pace
97%
Most learners liked this course
What you'll 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
Details to know
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Assessments
14 assignments
Taught in English
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There are 3 modules in this course
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).
What's included
13 videos15 readings5 assignments2 discussion prompts
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.
What's included
10 videos10 readings5 assignments1 discussion prompt
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.
What's included
9 videos7 readings4 assignments1 discussion prompt1 plugin
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Recommended if you're interested in Machine Learning
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Frequently asked questions
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.
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.
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.