Machine Learning at the Edge on Arm: A Practical Introduction from edX

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 artificial intelligence , machine learning and machine learning at the edge , and illustrate why businesses now need this technology to be available on connected devices. We’ll then introduce you to the concept of datasets and how to train algorithms using tools like Anaconda and Python. We'll then go on to explore advanced topics in machine learning 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 machine learning applications, such as speech and pattern recognition, as well as image processing, using actual sensor data obtained from the microcontroller. We'll also introduce you to the open source TensorFlow Python library, which is useful in the training and inference of deep neural networks.

In the final module you’ll be able to apply what you’ve learned by implementing machine learning 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 machine learning at the edge. Be a part of this vibrant community of developers and start your machine learning journey by enrolling in our course today!

What's inside

Learning objectives

An understanding of artificial intelligence, machine learning and machine learning 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.

An understanding of artificial intelligence, machine learning and machine learning 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 Machine Learning such as datasets, data analysis and 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

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Teaches you sophisticated microcontrollers technology

Designed for developers of connected devices who want to implement machine learning on devices

Gives learners an edge in understanding innovation and groundbreaking topics in machine learning, such as CNNs

Taught by instructors who are recognized for their work in the topic that this course teaches

You will apply what you have learned in this course by implementing machine learning algorithms on a dataset of your choice

Students need to purchase hardware for this course, which may be a financial barrier

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Machine Learning at the Edge on Arm: A Practical Introduction with these activities:

Complete Warm-up Questions for Module 1

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Become familiar with basic concepts of machine learning and artificial intelligence.

Browse courses on Machine Learning at the Edge

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Read the "What is Machine Learning" section in the module 1 materials
Complete the warm-up questions at the end of the section
Review your answers and make sure you understand the concepts

Attend a Study Group for Module 3

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Deepen your understanding of artificial neural networks by discussing concepts and solving problems with peers.

Browse courses on Artificial Neural Networks

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Find or join a study group for Module 3
Attend the study group sessions regularly
Actively participate in discussions and problem-solving activities

Explore TensorFlow Python Library

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Gain hands-on experience with a widely used machine learning library, TensorFlow, which is essential for training and deploying deep neural networks.

Browse courses on TensorFlow

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Follow the TensorFlow Python Tutorial
Work through the exercises and examples provided in the tutorial
Experiment with different Tensorflow functions and modules

Four other activities

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Develop a Cheat Sheet for Convolutional Neural Networks (CNNs)

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Solidify your understanding of CNNs by creating a concise and informative cheat sheet that summarizes key concepts and equations.

Browse courses on Convolutional Neural Networks

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Review the materials on CNNs from Module 5
Identify the most important concepts, equations, and examples
Organize and present the information in a clear and concise manner

Collate Resources on Machine Learning Optimization for Edge Devices

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Deepen your knowledge and understanding of machine learning optimization techniques specifically tailored for edge devices by compiling a collection of relevant resources.

Browse courses on Machine Learning Optimization

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Search for and identify research papers, articles, and tutorials on machine learning optimization for edge devices
Organize the resources into a structured and accessible format
Share the compilation with peers or publish it online for wider dissemination

Participate in a Workshop on Machine Learning Deployment

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Gain practical experience and insights into deploying machine learning models on edge devices by attending a dedicated workshop.

Browse courses on Machine Learning Deployment

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Identify and register for a relevant machine learning deployment workshop
Attend the workshop and actively participate in the exercises and discussions
Apply the knowledge and skills gained to your own projects or research

Build a Machine Learning Model for Speech Recognition

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Apply your knowledge and skills to a practical project by building a machine learning model that can recognize spoken words or phrases.

Browse courses on Speech Recognition

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Gather a dataset of audio recordings containing spoken words or phrases
Preprocess the audio data to extract relevant features
Train a machine learning model using the preprocessed data
Evaluate the performance of the model and make necessary adjustments
Deploy the model and test its accuracy in real-world scenarios

Career center

Learners who complete Machine Learning at the Edge on Arm: A Practical Introduction will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

Machine Learning Engineers use their mastery of machine learning and artificial intelligence to solve complex problems and build innovative solutions. This course, Machine Learning at the Edge on Arm: A Practical Introduction, would provide you with the foundational knowledge and practical skills to pursue this exciting career path. Through hands-on lab exercises, you'll gain experience in implementing machine learning algorithms on real-world datasets, preparing you to excel as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

Data Scientists play a crucial role in transforming raw data into valuable insights and actionable recommendations. This course will equip you with the essential skills to succeed in this field. You'll learn the fundamentals of machine learning, data analysis, and artificial intelligence, enabling you to extract meaningful insights from complex data and drive informed decision-making as a Data Scientist.

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Computer Vision Engineer

Computer Vision Engineers specialize in developing and implementing computer vision systems that enable computers to interpret and understand visual data. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a solid foundation in computer vision concepts and techniques. You'll gain hands-on experience in deploying computer vision models using Convolutional Neural Networks (CNNs), preparing you for success in this rapidly growing field.

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AI Engineer

AI Engineers are responsible for designing, developing, and deploying artificial intelligence systems. Enrolling in Machine Learning at the Edge on Arm: A Practical Introduction will empower you with the knowledge and skills to excel in this field. You'll gain a comprehensive understanding of AI concepts, machine learning techniques, and their practical applications, equipping you to contribute to cutting-edge AI solutions.

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Software Engineer

Software Engineers are in high demand due to the increasing reliance on technology across industries. This course, Machine Learning at the Edge on Arm: A Practical Introduction, can enhance your software engineering skills by providing you with a foundation in machine learning and AI. You'll learn to integrate machine learning models into software applications, enabling you to develop innovative and intelligent solutions.

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Data Analyst

Data Analysts play a vital role in analyzing data to extract insights and inform decision-making. Machine Learning at the Edge on Arm: A Practical Introduction will provide you with a strong foundation in machine learning and data analysis techniques. You'll learn to apply machine learning algorithms to real-world datasets, equipping you with the skills to succeed as a Data Analyst.

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AI Researcher

AI Researchers are at the forefront of advancing the field of artificial intelligence. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a solid foundation in machine learning concepts, algorithms, and techniques. You'll gain experience in implementing machine learning models on real-world data, preparing you to contribute to groundbreaking research in the field of AI.

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Machine Learning Architect

Machine Learning Architects design and implement machine learning solutions that meet specific business requirements. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a comprehensive understanding of machine learning principles and best practices. You'll learn to design, build, and deploy robust machine learning systems, enabling you to succeed as a Machine Learning Architect.

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Robotics Engineer

Robotics Engineers combine their expertise in mechanical engineering, electrical engineering, and computer science to design, build, and maintain robots. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a foundation in machine learning and AI, enabling you to develop intelligent robots that can perform complex tasks and interact with the real world.

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Embedded Systems Engineer

Embedded Systems Engineers design and develop small, computerized systems that are embedded into larger products. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with the knowledge and skills to integrate machine learning capabilities into embedded systems. You'll learn to optimize machine learning models for resource-constrained environments, enabling you to develop innovative and intelligent embedded systems.

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Cloud Architect

Cloud Architects design and manage cloud computing systems. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a foundation in machine learning and cloud computing. You'll learn to integrate machine learning models into cloud-based applications, enabling you to develop scalable and intelligent solutions.

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Business Intelligence Analyst

Business Intelligence Analysts use data analysis and visualization to provide insights that inform business decisions. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with the skills to apply machine learning techniques to business data. You'll learn to extract meaningful insights and develop predictive models that can help businesses make better decisions.

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Product Manager

Product Managers are responsible for managing the development and launch of new products. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with a foundation in machine learning and its applications in product development. You'll learn to evaluate the potential of machine learning for new products and services, enabling you to make informed decisions and drive innovation.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to assess risk and make investment decisions. This course, Machine Learning at the Edge on Arm: A Practical Introduction, will provide you with the skills to apply machine learning techniques to financial data. You'll learn to develop predictive models and make informed investment decisions, enhancing your potential as a Quantitative Analyst.

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Technical Writer

Technical Writers create technical documentation for software, hardware, and other products. This course, Machine Learning at the Edge on Arm: A Practical Introduction, may be useful for aspiring Technical Writers who want to specialize in documenting machine learning systems. You'll gain a foundational understanding of machine learning concepts and techniques, enabling you to effectively convey complex technical information to a non-technical audience.

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