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
Introduction to Embedded Machine Learning
Instructors: Shawn Hymel
Sponsored by Taipei Medical University [C4CB]
47,274 already enrolled
Gain insight into a topic and learn the fundamentals.
4.8
(681 reviews)
Intermediate level
Recommended experience
Flexible schedule
Approx. 17 hours
Learn at your own pace
96%
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
Skills you'll gain
- Applied Machine Learning
- Artificial Neural Networks
- Artificial Intelligence
- Computer Systems
- Computer Science
- Machine Learning
- Statistical Machine Learning
- Software Systems
- Artificial Intelligence and Machine Learning (AI/ML)
- Machine Learning Algorithms
- Deep Learning
- Embedded Systems
- Embedded Software
- System Software
- Machine Learning Methods
- Feature Engineering
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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4.8
681 reviews
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Showing 3 of 681
A
5
Reviewed on Jul 11, 2022
Highly recommended for anyone looking for embedded AI. For any starter, intermediate or advanced guys!
SS
5
Reviewed on Sep 23, 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.
RS
5
Reviewed on Mar 3, 2021
Very good arrange of topics and explain complex topics as simply as possible. Recommended course for anyone who needs to start in embedded machine learning.
Recommended if you're interested in Data Science
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