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Introduction to Embedded Machine Learning

Impulso Edge

Acerca de este Curso

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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.

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100 % en línea

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Nivel intermedio

Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).

Aprox. 17 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Qué aprenderás

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

Habilidades que obtendrás

Arduino
Machine Learning
Embedded System Design
Microcontroller
Computer Programming

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Certificado para compartir

Obtén un certificado al finalizar

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Nivel intermedio

Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).

Aprox. 17 horas para completar

Inglés (English)

¿Podría tu empresa beneficiarse de la capacitación de los empleados en las habilidades más demandadas?

Prueba Coursera para negocios

Instructores

Calificación del instructor

4,83/5 (201 calificaciones)

Shawn Hymel

Instructor

30.239 alumnos

2 cursos

Alexander Fred-Ojala

24.314 alumnos

1 curso

ofrecido por

Impulso Edge

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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Programa - Qué aprenderás en este curso

Calificación del contenido96%(1,688 calificaciones)

Semana

Semana 1

6 horas para completar

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 horas para completar

13 videos (Total 107 minutos), 14 lecturas, 5 cuestionarios

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 lecturas

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 ejercicios de práctica

Machine Learning and Limitations15m

Embedded Machine Learning15m

Data Collection15m

Feature Extraction15m

Machine Learning Overview30m

Semana

Semana 2

6 horas para completar

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 horas para completar

10 videos (Total 77 minutos), 10 lecturas, 5 cuestionarios

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 lecturas

Neural Networks and Training5m

Slides10m

Evaluation, Underfitting, and Overfitting5m

Slides10m

Using a Model for Inference5m

Slides10m

Anomaly Detection5m

Slides10m

Project - Motion Detection2h

Slides10m

5 ejercicios de práctica

Neural Networks and Training15m

Evaluation, Underfitting, and Overfitting15m

Deploy Model to Embedded System15m

Anomaly Detection5m

Motion Classification and Anomaly Detection30m

Semana

Semana 3

6 horas para completar

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 horas para completar

9 videos (Total 75 minutos), 7 lecturas, 4 cuestionarios

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 lecturas

Sample Rate and Bit Depth5m

Slides10m

MFCCs and CNNs5m

Slides10m

Implementation Strategies and Sensor Fusion5m

Slides10m

Project - Sound Classification2h

4 ejercicios de práctica

Audio Classification and Sampling Audio Signals10m

MFCCs and CNNs15m

Implementation Strategies15m

Audio Classification30m

Reseñas

4.8

115 reseña

5 stars
81,85 %
4 stars
16,19 %
3 stars
1,72 %
1 star
0,21 %

Principales reseñas sobre INTRODUCTION TO EMBEDDED MACHINE LEARNING

por SS23 de sep. de 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.

por EL27 de feb. de 2021

A great introduction to tinyML and embedded machine learning using Edge Impulse to get started working on projects immediately.

por SJ18 de ago. de 2021

Awesome course for beginners. I don't know how much of my background helped make this awesome, but it is awesome.

por JH9 de jun. de 2021

A really good introduction to Embedded Machine Learning! Shows the basics of machine learning and teaches how to apply it yourself with only the need of your smartphone. Totally recommended!

Ver todas las reseñas

Preguntas Frecuentes

¿Cuándo podré acceder a las lecciones y tareas?
El acceso a las clases y las asignaciones depende del tipo de inscripción que tengas. Si tomas un curso en modo de oyente, verás la mayoría de los materiales del curso en forma gratuita. Para acceder a asignaciones calificadas y obtener un certificado, deberás comprar la experiencia de Certificado, ya sea durante o después de participar como oyente. Si no ves la opción de oyente:
es posible que el curso no ofrezca la opción de participar como oyente. En cambio, puedes intentar con una Prueba gratis o postularte para recibir ayuda económica.
Es posible que el curso ofrezca la opción 'Curso completo, sin certificado'. Esta opción te permite ver todos los materiales del curso, enviar las evaluaciones requeridas y obtener una calificación final. También significa que no podrás comprar una experiencia de Certificado.
¿Qué recibiré si compro el Certificado?
Cuando compras un Certificado, obtienes acceso a todos los materiales del curso, incluidas las tareas calificadas. Una vez que completes el curso, se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes participar del curso como oyente sin costo.
¿Hay ayuda económica disponible?
Sí. En ciertos programas de aprendizaje, puedes postularte para recibir ayuda económica o una beca en caso de no poder costear los gastos de la tarifa de inscripción. Si hay ayuda económica o becas disponibles para tu selección de programa de aprendizaje, verás un enlace para postularte en la página de descripción.
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.

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Introduction to Embedded Machine Learning

Introduction to Embedded Machine Learning

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructores

Shawn Hymel

Alexander Fred-Ojala

ofrecido por

Impulso Edge

Programa - Qué aprenderás en este curso

Introduction to Machine Learning

Introduction to Neural Networks

Audio classification and Keyword Spotting

Reseñas

Principales reseñas sobre INTRODUCTION TO EMBEDDED MACHINE LEARNING

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Introduction to Embedded Machine Learning

Introduction to Embedded Machine Learning

Acerca de este Curso

Qué aprenderás

Habilidades que obtendrás

Instructores

Shawn Hymel

Alexander Fred-Ojala

ofrecido por

Impulso Edge

Programa - Qué aprenderás en este curso

Introduction to Machine Learning

Introduction to Neural Networks

Audio classification and Keyword Spotting

Reseñas

Principales reseñas sobre INTRODUCTION TO EMBEDDED MACHINE LEARNING

Preguntas Frecuentes

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Comienza o impulsa tu carrera profesional

Buscar temas populares

Cursos y artículos populares

Obtén un título de grado o un certificado en línea

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