This course will provide you with the hands-on experience you’ll need to create innovative ML applications using ubiquitous Arm-based microcontrollers. 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.
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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.
This course is part of the [Advanced Embedded Systems on Arm Professional Certificate.
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.
Syllabus
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
