Introduction to Embedded Machine Learning
Learn to deploy machine learning models on microcontrollers and create smart embedded systems with hands-on projects using Arduino.
Learn to deploy machine learning models on microcontrollers and create smart embedded systems with hands-on projects using Arduino.
This comprehensive course introduces embedded machine learning, focusing on deploying ML models to microcontrollers. You'll learn how machine learning works, train neural networks, and implement TinyML solutions. The course covers fundamental ML concepts, hardware considerations, and practical applications in motion and audio classification. Through hands-on projects with Arduino and Edge Impulse, you'll gain experience in data collection, feature extraction, model training, and deployment. No prior ML knowledge is required, though basic math skills and familiarity with embedded systems are recommended.
4.8
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English
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What you'll learn
Learn the basics of machine learning systems and their limitations
Master deploying ML models to microcontrollers
Understand how to use ML for embedded system decisions
Gain practical experience with neural networks and training
Explore audio classification and keyword spotting
Develop skills in feature extraction and model evaluation
Skills you'll gain
This course includes:
258 Minutes PreRecorded video
14 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to embedded machine learning, focusing on deploying ML models to microcontrollers. Through three modules, students learn the fundamentals of machine learning, neural networks, and their implementation on embedded systems. The curriculum covers practical applications including motion classification and audio processing, with hands-on projects using Arduino hardware and Edge Impulse platform. Students gain experience in data collection, feature extraction, model training, and deployment, preparing them for real-world embedded ML applications.
Introduction to Machine Learning
Module 1 · 5 Hours to complete
Introduction to Neural Networks
Module 2 · 6 Hours to complete
Audio classification and Keyword Spotting
Module 3 · 5 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructors
AI and Blockchain Pioneer in Academia and Industry
Alexander Fred-Ojala is a distinguished technology leader who serves as the CEO and Co-founder of Predli, a global emerging technology advisory firm, while maintaining significant academic roles at UC Berkeley. As the AI and Blockchain Director of the Learn2Launch Program and former Research Director of the Data Lab at UC Berkeley's Sutardja Center for Entrepreneurship & Technology, he has shaped the education of hundreds of engineering students through data science and blockchain courses. His expertise was recognized with the prestigious Amazon Alexa Innovation Fellowship in 2018-2019, making him one of just ten faculty members selected globally. His entrepreneurial experience includes being part of founding teams for multiple successful ventures, including Wheely's (YC 2015), Auranest (Series A), and InnoQuant, where he served as Co-founder & COO
Technical Content Developer and Electronics Education Expert
Shawn Hymel is an accomplished technical educator and content developer who specializes in making complex electronics and programming concepts accessible to learners of all ages. As the founder of Skal Risa, LLC since 2017, he creates educational videos, blogs, and courses for various technology clients. His professional background includes engineering positions at SparkFun Electronics, where he later transitioned into video production and marketing advisory roles. Currently, he serves as an instructor at Edge Impulse, where he teaches courses on embedded machine learning and computer vision. His contributions to technical education include developing comprehensive course materials on Real-Time Operating Systems (RTOS) and other advanced electronics topics. Beyond his professional work, he maintains an active presence in the electronics community by conducting workshops, and balances his technical pursuits with recreational interests like swing dancing.
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4.8 course rating
662 ratings
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