Computer Vision with Embedded Machine Learning

Edge Impulse introduces image classification, convolutional networks and object detection in an embedded-machine-learning context. The sequence connects visual models with the practical constraints of running them near the data source. It is useful for comparing a device-based workflow with cloud inference.

What you will learn

• Explain an image-classification workflow for an embedded setting.
• Describe how convolutional networks process images.
• Compare classification with object detection.

Who this suits

Intermediate learners comfortable with plots, arithmetic and algebra. Python experience is recommended for the projects.

Before you enroll

Check which hardware, datasets and Edge Impulse account features the projects require. Memory, latency and power constraints can change the choice of model even when accuracy is similar.

Coursera offers paid enrollment or subscription access. Check the enrollment screen for assessment and certificate terms, trials and any financial-aid options. Provider access terms can change; review them before paying.

Put it into practice

Compare two candidate models against a device’s memory and response-time limits. Document the trade-off you would accept before testing on hardware.

Source and review

This overview is based on the provider's published information, checked on 12 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at Coursera.

How YellowCourse describes and checks learning resources.