Computer Vision with Embedded Machine Learning
Computer vision (CV) is an exciting topic of research that aims to automate the process of assigning meaning to digital pictures and videos. To put it another way, we're assisting computers in seeing and understanding the world around them! To complete CV tasks, a variety of machine learning (ML) algorithms and techniques can be used, and as ML becomes faster and more efficient, you are able to deploy these techniques to embedded systems.
This course will teach you how to use deep learning with neural networks to classify images and detect objects in images and videos, thanks to a collaboration between Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation. You'll be able to use these machine learning models to deploy them on embedded systems.
This course introduces the ideas and terminology needed to comprehend how convolutional neural networks (CNNs) function, as well as how to utilize them to categorize pictures and recognize objects. You will be able to train your own CNNs and deploy them on a microcontroller and/or single board computer through the hands-on projects.
This course offers:
- Flexible deadlines: Reset deadlines in accordance to your schedule.
- Certificate: Earn a Certificate upon completion
- 100% online
- Intermediate Level
- Approx. 31 hours to complete
- Subtitles: English
Coursera Rating: 4.7/5
Enroll here: https://www.coursera.org/learn/computer-vision-with-embedded-machine-learning
