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Machine Learning at the Edge on Arm: A Practical Introduction

This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers.

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There is one session available:

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After a course session ends, it will be archivedOpens in a new tab.
Starts Sep 1

Machine Learning at the Edge on Arm: A Practical Introduction

This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers.

6 weeks
3–6 hours per week
Self-paced
Progress at your own speed
Free
Optional upgrade available

There is one session available:

After a course session ends, it will be archivedOpens in a new tab.
Starts Sep 1

About this course

Skip About this course

The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

To improve efficiency and performance, developers are now looking to analyse this data directly on the source device – usually a microcontroller (we call this ‘the Edge[’). But with this approach comes the challenge of implementing machine learning on devices that have constrained computing resources.

This is where our course can help!

By enrolling in Machine Learning at th e Edge on Arm: A Practical Introduction you’ll learn how to train machine learning models and implement them on industry relevant Arm-based microcontrollers.

We’ll start your learning journey by taking you through the basics of AI, ML and ML at the Edge, and illustrate why businesses now need this technology to be available on tiny devices. We’ll then introduce you to the concept of datasets and how to train ML algorithms to recognize patterns, before exploring advanced topics such as Artificial Neural Networks and Computer Vision.

Along the way, our practical lab exercises will show you how you can address real-world design problems in deploying ML applications, such as motion and speech recognition, as well as image processing, using actual sensor data obtained from the microcontroller.

In the final module you’ll be able to apply what you’ve learned by implementing ML algorithms on a dataset of your choice.

The ST DISCO-L475E board used in this course can be purchased directly from our technology partner STMicroelectronics: https://www.st.com/content/st_com/en/campaigns/educationalplatforms/iot-arm-edx-edu.html

Through our vast ecosystem, Arm already powers a wide range of devices and applications that rely on ML at the Edge. Be a part of this vibrant community of developers and start your machine learning journey by enrolling in our course today!

At a glance

  • Language: English
  • Video Transcript: English

What you'll learn

Skip What you'll learn
  • An understanding of Artificial Intelligence, Machine Learning and ML concepts.
  • How to get started with machine learning on Arm microcontrollers.
  • How to acquire data from sensors and peripherals on a microcontroller.
  • The fundamentals of Artificial Neural Networks in constrained environments.
  • Convolutional Neural Networks and Deep Learning.
  • How to deploy computer vision models using CMSIS-NN.

Module 1 - Understand basic concepts of AI, ML and Edge ML.

Module 2 - Identify the key features of ML such as datasets, data analysis and ML alogorithm training.

Module 3 - Learn to explain the basic elements of Artificial Neural Networks.

Module 4 - Learn to explain the basic elements of Convolutional Neural Networks (CNN).

Module 5 - Understand how to deploy computer vision using CNN.

Module 6 - Learn to optimise ML models under the constraints of a microcontroller environment

About the instructors

Who can take this course?

Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.

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