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TinyML Foundations: Machine Learning for Embedded Systems

Learn the fundamentals of Tiny Machine Learning (TinyML) for embedded systems and smart devices in this comprehensive course.

Learn the fundamentals of Tiny Machine Learning (TinyML) for embedded systems and smart devices in this comprehensive course.

This foundational course introduces Tiny Machine Learning (TinyML), a rapidly growing field at the intersection of embedded systems and machine learning. Students will explore the fundamentals of machine learning, deep learning, and their applications in embedded devices like smartphones. The course covers essential concepts including data collection, model training, deployment strategies, and responsible AI design. With a focus on practical applications, students will learn to understand and work with TinyML systems, preparing them for advanced applications in embedded machine learning.

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TinyML Foundations: Machine Learning for Embedded Systems

This course includes

5 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

25,972

Audit For Free

What you'll learn

  • Master fundamental concepts of machine learning and deep learning

  • Understand data collection and preparation for ML applications

  • Learn techniques for training and deploying ML models

  • Grasp the principles of embedded machine learning systems

  • Develop skills in responsible AI design and implementation

  • Prepare for advanced TinyML applications and development

Skills you'll gain

TinyML
Machine Learning
Deep Learning
Embedded Systems
Data Science
Computer Vision
Model Training
AI Ethics
Python Programming

This course includes:

PreRecorded video

Graded assignments, exams

Access on Mobile, Tablet, Desktop

Limited Access access

Shareable certificate

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There are 2 modules in this course

This introductory course provides a comprehensive foundation in Tiny Machine Learning (TinyML), focusing on the intersection of machine learning and embedded systems. The curriculum covers fundamental concepts of machine learning and deep learning, specifically tailored for implementation on embedded devices. Students learn about data collection techniques, model training processes, and deployment strategies while emphasizing responsible AI development. The course bridges theoretical knowledge with practical applications, preparing students for advanced TinyML implementations.

Welcome to TinyML

Module 1

Introduction to (Tiny) ML

Module 2

Fee Structure

Instructors

Laurence Moroney
Laurence Moroney

5 rating

9 Reviews

5,22,923 Students

19 Courses

Pioneering AI Educator and Best-Selling Author

Laurence Moroney is an award-winning artificial intelligence researcher and best-selling author dedicated to making AI and machine learning accessible to everyone. As an instructor at DeepLearning.AI, he has taught millions through MOOCs and YouTube, while also serving as a keynote speaker at various events. Moroney is a fellow of the AI Fund and advises several AI startups, leveraging his expertise to foster innovation in the field. Based in Seattle, Washington, he is also an active member of the Science Fiction Writers of America, having authored multiple sci-fi novels and comic books. When not immersed in technology, he enjoys indulging in coffee and exploring creative writing.

A Pioneer in Computer Architecture and Machine Learning Systems

Dr. Vijay Janapa Reddi serves as the John L. Loeb Associate Professor of Engineering and Applied Sciences at Harvard University and Vice President of MLCommons, where he drives innovation in machine learning as both co-founder and research chair. His research integrates computer architecture and machine learning systems to advance intelligence and autonomy in mobile devices, edge computing platforms, and IoT devices. After completing his BS in Computer Engineering from Santa Clara University, MS from the University of Colorado Boulder, and PhD in Computer Science from Harvard University, he established himself at the University of Texas at Austin before joining Harvard in 2019. His significant contributions include co-leading the development of MLPerf benchmarks, creating the Tiny Machine Learning series on edX reaching thousands of global learners, and pioneering work in mobile and edge computing systems. His exceptional achievements have earned him numerous accolades, including the NAE Gilbreth Lecturer Honor, IEEE TCCA Young Computer Architect Award, Intel Early Career Award, multiple Google Faculty Research Awards, and induction into both the MICRO and HPCA Halls of Fame. Beyond academia, he serves on the boards of MLCommons and the tinyML Foundation, while actively working to democratize machine learning education through initiatives like the Austin Independent School District's hands-on computer science program and the development of open-source educational resources.

TinyML Foundations: Machine Learning for Embedded Systems

This course includes

5 Weeks

Of Self-paced video lessons

Beginner Level

Completion Certificate

awarded on course completion

25,972

Audit For Free

Testimonials

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Frequently asked questions

Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.