Edge Impulse
Introduction to Embedded Machine Learning
Edge Impulse

Introduction to Embedded Machine Learning

Taught in English

Some content may not be translated

42,832 already enrolled

Course

Gain insight into a topic and learn the fundamentals

Shawn Hymel
Alexander Fred-Ojala

Instructors: Shawn Hymel

4.8

(626 reviews)

Intermediate level

Recommended experience

17 hours to complete
3 weeks at 5 hours a week
Flexible schedule
Learn at your own pace

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 quizzes

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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 quizzes2 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 quizzes1 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 quizzes1 discussion prompt1 plugin

Instructors

Instructor ratings
4.8 (249 ratings)
Shawn Hymel
Edge Impulse
2 Courses54,617 learners

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Edge Impulse

Recommended if you're interested in Machine Learning

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