4.7
stars
88 ratings

Shawn Hymel

11,875 already enrolled

Offered By

Computer Vision with Embedded Machine Learning

Edge Impulse

About this Course

18,642 recent views

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.
This course, offered by a partnership among Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation, will give you an understanding of how deep learning with neural networks can be used to classify images and detect objects in images and videos. You will have the opportunity to deploy these machine learning models to embedded systems, which is known as embedded machine learning or TinyML.

Familiarity with the Python programming language and basic ML concepts (such as neural networks, training, inference, and evaluation) is advised to understand some topics as well as complete the projects. Some math (reading plots, arithmetic, algebra) is also required for quizzes and projects. If you have not done so already, taking the "Introduction to Embedded Machine Learning" course is recommended.

This course covers the concepts and vocabulary necessary to understand how convolutional neural networks (CNNs) operate, and it covers how to use them to classify images and detect objects. The hands-on projects will give you the opportunity to train your own CNNs and deploy them to a microcontroller and/or single board computer.

Flexible deadlines

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Shareable Certificate

Earn a Certificate upon completion

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Coursera Labs

Includes hands on learning projects.

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Intermediate Level

Some math (reading plots, arithmetic, and algebra) is required in the course. Experience with the Python is recommended to complete the projects.

Approx. 30 hours to complete

English

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What you will learn

How to train and develop an image classification system using machine learning
How to train and develop an object detection system using machine learning
How to deploy a machine learning model to a microcontroller

Skills you will gain

Machine Learning
Python Programming
Microcontroller
Embedded System Development
Computer Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Coursera Labs

Includes hands on learning projects.

Learn more about Coursera Labs

Intermediate Level

Some math (reading plots, arithmetic, and algebra) is required in the course. Experience with the Python is recommended to complete the projects.

Approx. 30 hours to complete

English

Could your company benefit from training employees on in-demand skills?

Try Coursera for Business

Instructor

Instructor rating

4.61/5 (26 Ratings)

Shawn Hymel

Instructor

35,379 Learners

2 Courses

Offered by

Edge Impulse

Edge Impulse is the leading development platform for machine learning on edge devices, free for developers and trusted by enterprises. Founded in 2019 by Zach Shelby and Jan Jongboom, we are on a mission to enable developers to create the next generation of intelligent devices. We believe that machine learning can enable positive change in society, and we are dedicated to support applications for good.

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Syllabus - What you will learn from this course

Week1

Week 1

11 hours to complete

Image Classification

11 hours to complete

13 videos (Total 185 min), 14 readings, 4 quizzes

13 videos

Welcome to the Course6mInstructor Introductions2mWhat is Computer Vision?14mOverview of Digital Images10mData Collection10mOverview of Image Classification15mReview of Neural Networks17mTraining an Image Classifier with Keras25mUsing Colab to Curate and Upload a Dataset18mUsing Edge Impulse to Train a Model9mInference on a Single Board Computer30mInference on a Microcontroller (MicroPython)17mReview of Module 15m

14 readings

Syllabus10mRequired Hardware10mGetting Help10mSlides10mSlides10mPython and Numpy Help10mProject - Load and Manipulate Images2hSlides10mImage Classification and Neural Networks10mPython and Edge Impulse Documentation10mProject - Extract Features and Train Model1hEdge Impulse and OpenMV Documentation10mProject - Deploy DNN Image Classifier2hSlides10m

4 practice exercises

Computer Vision10mImage Classification with Neural Networks15mImage Classification on Embedded Devices10mModule 1 Review30m

Week2

Week 2

11 hours to complete

Convolutional Neural Networks

In this module, we go over the basics of convolutional neural networks (CNNs) and how they can be used to create a more robust image classification model. We look at the internal workings of CNNs (e.g. convolution and pooling) along with some visualization techniques used to see how CNNs make decisions. We introduce the concept of data augmentation to help provide more data to the training process. You will have the opportunity to train your own CNN and deploy it to an embedded system.

11 hours to complete

9 videos (Total 114 min), 13 readings, 5 quizzes

9 videos

Image Convolution11mPooling Layer6mConvolutional Neural Network14mTraining a Convolutional Neural Network15mCNN Visualizations15mData Augmentation15mTransfer Learning and MobileNet12mTransfer Learning with Edge Impulse15mReview of Module 26m

13 readings

Slides10mProject - Convolution and Pooling2hDigging Deeper into CNNs10mSlides10mProject - Training a CNN1h 10mSlides10mCNN Visualizations and Data Augmentation10mProject - Data Augmentation1hDigging Deeper into Transfer Learning10mSlides10mProject - Transfer Learning1hProject - Deploy CNN Image Classifier1hSlides10m

5 practice exercises

Convolution and Pooling15mConvolutional Neural Networks10mVisualizations and Data Augmentation5mTransfer Learning10mModule 2 Review30m

Week3

Week 3

9 hours to complete

Object Detection

In this module, we will cover the basics of object detection and how it differs from image classification. We will go over the math involved to measure objection detection performance. After, we will introduce several popular object detection models and demonstrate the process required to train such a model in Edge Impulse. Finally, you will be asked to deploy an object detection model to an embedded system.

9 hours to complete

10 videos (Total 109 min), 10 readings, 3 quizzes

10 videos

Introduction to Object Detection13mObject Detection Performance Metrics11mObject Detection Models16mTraining an Object Detection Model16mDeploy Object Detection Model to a Single Board Computer10mImage Segmentation7mMulti-stage Inference with Dmitry Maslov6mReusing Representations with Mat Kelcey14mReview of Module 37mConclusion3m

10 readings

Slides10mDrawing API10mProject - Sliding Window Object Detection2hSlides10mDigging Deeper into Object Detection10mDeploying an Object Detection Model10mSlides10mDigging Deeper into Advanced Topics10mProject - Deploy Object Detection Model2hSlides10m

3 practice exercises

Object Detection20mImage Segmentation10mModule 3 Review30m

Reviews

4.7

16 reviews

5 stars
76.13%
4 stars
21.59%
3 stars
2.27%

TOP REVIEWS FROM COMPUTER VISION WITH EMBEDDED MACHINE LEARNING

by SGNov 2, 2022

3rd week was pretty fast and a lot more information can be added in it,

i think the course should be 4th week long.

still one of the best course to done

by MLSep 23, 2021

Great course, Shawn always explains things in a clear and engaging way, with a strong focus on the application of the concepts. I'm definitely looking forward to more courses on embedded ML!

View all reviews

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Computer Vision with Embedded Machine Learning

Computer Vision with Embedded Machine Learning

About this Course

What you will learn

Skills you will gain