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 ImpulseÃœber diesen Kurs
53.161 kürzliche Aufrufe
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
Ca. 17 Stunden zum Abschließen
Englisch
Untertitel: Englisch
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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
Kompetenzen, die Sie erwerben
- Arduino
- Machine Learning
- Embedded System Design
- Microcontroller
- Computer Programming
Flexible Fristen
Setzen Sie Fristen gemäß Ihrem Zeitplan zurück.
Zertifikat zur Vorlage
Erhalten Sie nach Abschluss ein Zertifikat
100Â % online
Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.
Stufe „Mittel“
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
Ca. 17 Stunden zum Abschließen
Englisch
Untertitel: Englisch
Könnte Ihr Unternehmen von Mitarbeiterweiterbildungen für gefragte Kompetenzen profitieren?
Probieren Sie Coursera for Business aus
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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.
Lehrplan - Was Sie in diesem Kurs lernen werden
6 Stunden zum Abschließen
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 Stunden zum Abschließen
13 Videos (Gesamt 107 min), 14 Lektüren, 5 Quiz
6 Stunden zum Abschließen
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 Stunden zum Abschließen
10 Videos (Gesamt 77 min), 10 Lektüren, 5 Quiz
6 Stunden zum Abschließen
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 Stunden zum Abschließen
9 Videos (Gesamt 75 min), 7 Lektüren, 4 Quiz
Bewertungen
- 5 stars82,75Â %
- 4 stars15,38Â %
- 3 stars1,63Â %
- 1 star0,23Â %
Top-Bewertungen von INTRODUCTION TO EMBEDDED MACHINE LEARNING
von SS23. Sep. 2021
Thanks for detailed and well introduced topics, I enjoyed this course. I had prior knowledge in neural networks but this course was awsome for introducting ML for microcontrollers.
von TN14. März 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.
von MZ20. Juli 2021
Best courser, as we are not just learning about the Embedded ML, as we also learn the fundamentals of ML.
von SA17. Feb. 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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