Edge AI for Microcontrollers
About this course
This specialization is intended for engineers of any discipline who want to upskill into edge AI. Throughout the three courses learners will learn the fundamentals of edge AI and how to apply them to sensor-based problems using both time-series and vision data. This specialization also explores how to build and deploy edge AI models for microcontrollers and addresses how to approach designing models for the hardware constraints of tiny devices.
67/100
CourseAsk score
- What the provider tells you
- 31/45
- Who stands behind it
- 20/35
- How complete the listing is
- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- Design and optimize AI models for microcontroller hardware constraints
- Process time-series sensor data for edge AI applications
- Implement computer vision models on resource-constrained devices
- Deploy trained models to microcontrollers and embedded systems
- Approach model architecture decisions based on memory and processing limitations
Course objectives
- Build foundational understanding of edge AI principles and constraints
- Apply machine learning techniques to sensor-based problems
- Deploy functional AI models on microcontroller hardware
Price shown by Coursera — confirm on their site.
Enroll on CourseraYou'll be redirected to Coursera to complete enrollment.
- Listed & compared by CourseAsk
- English · 0
Coursera
Udemy
Compared on these lists
Where this course ranks against the alternatives.