Intro to Embedded Machine Learning paid course free. Embedded Systems, Machine Learning, and Tiny ML. 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 Systems
- Machine Learning
- Embedded Machine Learning
- Access to Thunderboard™ Sense 2: IoT Development Kit
Intro to Embedded Machine Learning Course Description
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 complex machine, learning models. Embedded devices used for machine learning applications can accomplish many tasks in the industry.
A typical example: sensor equipment that detects acoustic or optical anomalies and differences, thereby supporting quality assurance in production or system health monitoring. In addition to cameras that monitor visual parameters and microphones that record sound waves, these devices also use sensors to detect information such as vibration, contact, voltage, current, speed, pressure, and temperature. Compared with machine learning, the educational content of embedded machine learning has not kept up.
This course attempts to fill this gap by providing basic knowledge of embedded systems, machine learning, and Tiny ML. The course will end with an interactive project where students can create their own professional integrated machine learning projects. This project will be based on using a microcontroller or your own mobile device to detect acoustic events.
At the end of the course, you will be able to choose your own classification and audio, and train and implement machine learning models yourself. This is a great way to introduce yourself and gain valuable experience in the field of embedded machine learning.
Who this course is for:
- Beginner students curious about embedded machine learning
Intro to Embedded Machine Learning