Computer Vision with Embedded Machine Learning

Go to Course: https://www.coursera.org/learn/computer-vision-with-embedded-machine-learning

Introduction

### Course Review: Computer Vision with Embedded Machine Learning In today's tech-driven world, the ability of machines to interpret visual data is more essential than ever. The **Computer Vision with Embedded Machine Learning** course offered on Coursera is an outstanding introduction to this cutting-edge field, making it both relevant and insightful for aspiring AI practitioners and enthusiasts alike. #### Overview This course is a collaborative effort from notable organizations—Edge Impulse, OpenMV, and Seeed Studio—bringing together a wealth of expertise and resources to provide a comprehensive curriculum focused on enabling computers to "see" and interpret their surroundings. The course not only introduces theoretical concepts but also emphasizes hands-on applications by deploying models on embedded systems. #### Course Syllabus The course is structured into three core modules, each designed to build upon the previous one, ensuring a robust learning path. 1. **Image Classification** - In this foundational module, learners explore the mechanics of computer vision, covering the creation and storage of digital images. The primary focus is on neural networks and their application in classifying images. The highlight of this segment is a project that allows participants to build an image classifier that is deployable on an embedded device, which offers invaluable practical experience. 2. **Convolutional Neural Networks (CNNs)** - Delving deeper, this module introduces CNNs, a key component of modern computer vision. Learners get to grips with essential techniques like convolution and pooling, alongside methods for visualizing CNN decision-making. Data augmentation is also a critical topic, ensuring that students understand how to enhance their datasets. Participants will even have the opportunity to train their CNN and deploy it directly on an embedded platform, reinforcing their knowledge through practical application. 3. **Object Detection** - The final module shifts focus to object detection, which is a more complex endeavor compared to standard image classification. Here, students learn how to measure the performance of detection models with mathematical frameworks, followed by an overview of popular object detection models. The practical aspect culminates in deploying a trained object detection model to an embedded device, solidifying the knowledge gained throughout the course. #### Review and Recommendations The **Computer Vision with Embedded Machine Learning** course stands out for several reasons: - **Practical Emphasis**: The hands-on projects in each module empower learners to apply theoretical knowledge in real-world scenarios. This is particularly beneficial for those looking to work in industries where embedded machine learning is becoming increasingly critical. - **Structured Learning Path**: The progression from image classification to more complex concepts like CNNs and object detection is logical and paced well, catering to both beginners and those with some prior experience in machine learning. - **Community and Resources**: Being part of a course that is backed by experienced organizations means access to a community of learners and industry experts, which is invaluable for networking and support. - **Industry-Relevant Skills**: With the surge in demand for skills related to AI and embedded systems, this course provides a competitive edge by focusing on the integration of computer vision with embedded machine learning, equipping you with sought-after competencies. In conclusion, I highly recommend the **Computer Vision with Embedded Machine Learning** course for anyone eager to explore the integration of machine learning and computer vision technology. Whether you're a student aiming to break into AI or a professional looking to upskill in modern technologies, this course offers a rich learning experience that prepares you for the challenges and innovations in the field. Don't miss out — enroll today to start your journey in helping machines see the world!

Syllabus

Image Classification

In this module, we introduce the concept of computer vision and how it can be used to solve problems. We cover how digital images are created and stored on a computer. Next, we review neural networks and demonstrate how they can be used to classify simple images. Finally, we walk you through a project to train an image classifier and deploy it to an embedded system.

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.

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.