Fundamentals of TinyML (edX)

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 be able to deploy code to your own physical TinyML device.
This course is part of the Tiny Machine Learning (TinyML) Professional Certificate.

What you'll learn

• Fundamentals of Machine Learning (ML)
• Fundamentals of Deep Learning
• How to gather data for ML
• How to train and deploy ML models
• Understanding embedded ML
• Responsible AI Design

Syllabus

• Overview of the TinyML Course Series
• Why the Future of Machine Learning is Tiny and Bright
• The Building Blocks of Machine Learning and Deep Learning
• Responsible Machine Learning Design