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.
提供：
Introduction to Embedded Machine Learning
エッジインパルスこのコースについて
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共有できる証明書
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100%オンライン
自分のスケジュールですぐに学習を始めてください。
中級レベル
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
約17時間で修了
英語
字幕：英語
学習内容
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
習得するスキル
- Arduino
- Machine Learning
- Embedded System Design
- Microcontroller
- Computer Programming
柔軟性のある期限
スケジュールに従って期限をリセットします。
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
中級レベル
Some math (reading plots, arithmetic, and algebra) is required in the course. Recommended to have experience with embedded systems (e.g. Arduino).
約17時間で修了
英語
字幕：英語
提供：
エッジインパルス
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.
シラバス - 本コースの学習内容
6時間で修了
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時間で修了
13件のビデオ （合計107分）, 14 学習用教材, 5 個のテスト
6時間で修了
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時間で修了
10件のビデオ （合計77分）, 10 学習用教材, 5 個のテスト
6時間で修了
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時間で修了
9件のビデオ （合計75分）, 7 学習用教材, 4 個のテスト
レビュー
- 5 stars82.92%
- 4 stars15.09%
- 3 stars1.73%
- 1 star0.24%
INTRODUCTION TO EMBEDDED MACHINE LEARNING からの人気レビュー
MKにより2021年3月31日
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.
JBにより2022年5月16日
This is an excellent course taught by the best Teachers on AI. I enjoyed the course and learnt from scratch about AI, ML, NN and deep learning.
TNにより2021年3月14日
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.
ELにより2021年2月27日
A great introduction to tinyML and embedded machine learning using Edge Impulse to get started working on projects immediately.
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