Individual Course
Fundamentals of TinyML
Course Length
5 weeks
2-4 hours per week
Featuring faculty from:
Harvard John A. Paulson School of Engineering and Applied Sciences
Enroll as Individual
Certificate Price:
$ 299
Enroll as Individual
Certificate Price:
$ 299
This online course focuses on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the language of TinyML.
What do you know about TinyML? Tiny Machine Learning (TinyML) is one of the fastest-growing areas of Deep Learning and is rapidly becoming more accessible. This course provides a foundation for you to understand this emerging field.
TinyML is at the intersection of embedded Machine Learning (ML) applications, algorithms, hardware, and software. TinyML differs from mainstream machine learning (e.g., server and cloud) in that it requires not only software expertise, but also embedded-hardware expertise.
The first course in the TinyML Certificate series, Fundamentals of TinyML will focus on the basics of machine learning, deep learning, and embedded devices and systems, such as smartphones and other tiny devices. Throughout the course, you will learn data science techniques for collecting data and develop an understanding of learning algorithms to train basic machine learning models. At the end of this course, you will be able to understand the “language” behind TinyML and be ready to dive into the application of TinyML in future courses.
Following Fundamentals of TinyML, the other courses in the TinyML Professional Certificate program will allow you to see the code behind widely-used Tiny ML applications—such as tiny devices and smartphones—and deploy code to your own physical TinyML device. Fundamentals of TinyML provides an introduction to TinyML and is not a prerequisite for Applications of TinyML or Deploying TinyML for those with sufficient machine learning and embedded systems experience.
The course will be delivered via edX and connect learners around the world.
Self-Guided
edX
Global Learning Community
- Learning Outcome
Fundamentals of Machine Learning (ML) and Deep Learning
- Learning Outcome
How to gather data for ML, and train and deploy ML models
- Learning Outcome
Responsible AI Design
- Learn from Harvard faculty
- Do it on your own time
- Get a certificate, add it to your resume
- Be part of the Harvard Community
Ways to take this course
Audit or Pursue a Verified Certificate
A Verified Certificate costs $299 and provides unlimited access to full course materials, activities, tests, and forums. At the end of the course, learners who earn a passing grade can receive a certificate.
Alternatively, learners can Audit the course for free and have access to select course material, activities, tests, and forums. Please note that this track does not offer a certificate for learners who earn a passing grade.
Faculty
Your Instructor
Laurence Moroney
Lead AI Advocate at Google
Laurence Moroney leads AI Advocacy at Google, working as part of the Google Research into Machine Intelligence (RMI) team. He's the author of more programming books than he can count, including 'AI and Machine Learning for Coders' with OReilly, published in October 2020.
Your Instructor
Vijay Janapa Reddi
Associate Professor at John A. Paulson, School of Engineering and Applied Sciences (SEAS) at Harvard University
Vijay Janapa Reddi is an Associate Professor at Harvard University, Inference Co-chair for MLPerf, and a founding member of MLCommons, a nonprofit ML (Machine Learning) organization aiming to accelerate ML innovation.
Being a part of the Harvard global community of learners is an incredibly honored and significant opportunity to me.
Ethan W.
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