Created by Ashvin Roharia | Last updated 4/2021
Duration: 45m | 6 sections | 20 lectures | Video: 1280×720, 44 KHz | 297 MB
Genre: eLearning | Language: English + Sub
Embedded Systems, Machine Learning, and Tiny ML
What you’ll learn
Embedded Machine Learning
Access to Thunderboard™ Sense 2: IoT Development Kit
In this course, you will learn more about the field of embedded machine learning. In recent years, technological advances in embedded systems have enabled microcontrollers to run complicated machine learning models. Embedded devices for machine learning applications can fulfill many tasks in the industry. One typical example: sensor devices that detect acoustic or optical anomalies and discrepancies and, in this way, support quality assurance in production or system condition monitoring. In addition to cameras for monitoring visual parameters and microphones for recording soundwaves, these devices also use sensors for, for instance, vibration, contact, voltage, current, speed, pressure, and temperature.
Even though there is plenty of educational content on embedded systems and machine learning individually, educational content on embedded ML has yet to catch up. This course attempts to fill that void by providing fundamentals of embedded systems, machine learning, and Tiny ML. This course will conclude with an interactive project where the learner will get to create their own specialized embedded ML project. This project will be based on acoustic event detection using a microcontroller or your own mobile device. By the end of the course, you will be able to pick your own classifications and audio and train and deploy a machine learning model yourself. This is a great way to introduce yourself to and gain valuable experience in the field of embedded machine learning.
Who this course is for:Beginner students curious about embedded machine learning