Machine Learning at the Edge on Arm: A Practical Introduction

Explore how machine learning works on real-world Arm-based devices.

Machine learning is increasingly being used directly on connected devices, especially where fast, efficient and low-power decision-making is required. This course introduces learners to the principles of artificial intelligence, machine learning and Edge ML, with a focus on deploying models on Arm-based microcontrollers.

Learners will explore datasets, model training, neural networks, computer vision and the practical constraints involved in running machine learning models on microcontroller environments.

To complete this training, learners must enrol through Arm Education.

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Free

Machine Learning at the Edge on Arm: A Practical Introduction

What you'll Learn

Understand the basic concepts of artificial intelligence, machine learning and Edge ML

Identify key machine learning features including datasets, data analysis and model training

Explain the basic elements of artificial neural networks

Understand the core principles of convolutional neural networks

Explore how computer vision can be deployed using CNN models

Learn how to optimise machine learning models for microcontroller environments

Skills you'll gain

Machine Learning Edge AI Artificial Intelligence Data Analysis Model Training Neural Networks Convolutional Neural Networks Computer Vision Microcontroller Deployment Model Optimisation

There are 6 modules in this course

Course Features

  • Intermediate to Advanced
  • 36+ hours of video content
  • 6 modules with over 0 lessons
  • Lifetime access with free updates
  • No prior programming experience required

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Reviews

Why People Choose Us for Their Career

S

Sarah Collins

Aston University

This course transformed the way I think about web development. The curriculum is well-structured and the instructor is very engaging.

E

Emily Roberts

Cambridge Wireless

Fantastic learning experience. I gained practical skills I could apply immediately in my role at Cambridge Wireless.

J

James Walker

Cambridge Wireless

The modules are clearly laid out and the projects really help solidify the concepts. Highly recommended.

O

Olivia Martinez

Homerton University

I was a complete beginner and now I feel confident building real projects. The support community is excellent.