Computer Vision with Embedded Machine Learning faq

instructor Instructor: Shawn Hymel instructor-icon
duration Duration: duration-icon

This course explores the field of Computer Vision (CV) and how it can be used to automate the process of assigning meaning to digital images or videos. It covers the use of Machine Learning (ML) algorithms and techniques to accomplish CV tasks, and how these techniques can be deployed to embedded systems.

ADVERTISEMENT

Course Feature Course Overview Pros & Cons Course Provider Discussion and Reviews
Go to class

Course Feature

costCost:

Free

providerProvider:

Coursera

certificateCertificate:

Paid Certification

languageLanguage:

English

start dateStart Date:

12th Jun, 2023

Course Overview

❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [March 20th, 2023]

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.
(Please note that we obtained the following content based on information that users may want to know, such as skills, applicable scenarios, future development, etc., combined with AI tools, and have been manually reviewed)
Learners can learn the following from this course:

1. Computer Vision (CV): Understand the fundamentals of CV and how to use ML algorithms and techniques to automate the process of assigning meaning to digital images or videos.
2. Embedded Machine Learning (TinyML): Learn how to deploy ML models to embedded systems and gain an understanding of deep learning with neural networks.
3. Python Programming: Become familiar with the Python programming language and basic ML concepts such as neural networks, training, inference, and evaluation.
4. Convolutional Neural Networks (CNNs): Gain an understanding of the concepts and vocabulary necessary to understand how CNNs operate, and learn how to use them to classify images and detect objects.

[Applications]
Upon completion of this course, participants should have a better understanding of how to use deep learning with neural networks to classify images and detect objects in images and videos. They should also have the skills to deploy these machine learning models to embedded systems. Participants should be able to apply these skills to their own projects and use them to create their own computer vision applications.

[Career Paths]
1. Computer Vision Engineer: Computer vision engineers are responsible for developing and deploying computer vision algorithms and systems. They must have a strong understanding of computer vision, machine learning, and embedded systems. They must also be able to design and implement efficient algorithms and systems that can be deployed to embedded systems. As the demand for computer vision applications increases, the need for computer vision engineers is expected to grow.

2. Machine Learning Engineer: Machine learning engineers are responsible for developing and deploying machine learning algorithms and systems. They must have a strong understanding of machine learning, computer vision, and embedded systems. They must also be able to design and implement efficient algorithms and systems that can be deployed to embedded systems. As the demand for machine learning applications increases, the need for machine learning engineers is expected to grow.

3. Embedded Systems Engineer: Embedded systems engineers are responsible for developing and deploying embedded systems. They must have a strong understanding of embedded systems, computer vision, and machine learning. They must also be able to design and implement efficient systems that can be deployed to embedded systems. As the demand for embedded systems increases, the need for embedded systems engineers is expected to grow.

4. TinyML Engineer: TinyML engineers are responsible for developing and deploying TinyML applications. They must have a strong understanding of TinyML, computer vision, and machine learning. They must also be able to design and implement efficient algorithms and systems that can be deployed to embedded systems. As the demand for TinyML applications increases, the need for TinyML engineers is expected to grow.

[Education Paths]
Recommended Degree Paths:
1. Bachelor of Science in Computer Science: This degree program provides students with a comprehensive understanding of computer science fundamentals, including programming, algorithms, data structures, operating systems, and computer architecture. It also covers topics such as artificial intelligence, machine learning, computer vision, and embedded systems. This degree is ideal for those interested in pursuing a career in computer vision and embedded machine learning.

2. Master of Science in Artificial Intelligence: This degree program focuses on the development of intelligent systems, including machine learning, natural language processing, computer vision, and robotics. It provides students with the skills and knowledge necessary to design, develop, and deploy AI-based systems. This degree is ideal for those interested in pursuing a career in computer vision and embedded machine learning.

3. Doctor of Philosophy in Computer Science: This degree program provides students with an in-depth understanding of computer science fundamentals, including programming, algorithms, data structures, operating systems, and computer architecture. It also covers topics such as artificial intelligence, machine learning, computer vision, and embedded systems. This degree is ideal for those interested in pursuing a career in computer vision and embedded machine learning research.

Developing Trends:
1. Edge Computing: Edge computing is a distributed computing paradigm that brings computation and data storage closer to the edge of the network, where data is generated and consumed. This technology is becoming increasingly important for computer vision and embedded machine learning applications, as it allows for faster and more efficient processing of data.

2. Autonomous Systems: Autonomous systems are systems that are capable of performing tasks without human intervention. This technology is becoming increasingly important for computer vision and embedded machine learning applications, as it allows for more efficient and accurate processing of data.

3. Deep Learning: Deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data. This technology is becoming increasingly important for computer vision and embedded machine learning applications, as it allows for more accurate and efficient processing of data.

Pros & Cons

Pros Cons
  • pros

    Clear and engaging explanations

  • pros

    Updated info and correct topics depth

  • pros

    Comprehensive and recommended

  • pros

    Great introduction to CV with EML

  • pros

    Amazing course

  • cons

    Not suitable without Edge Impulse

  • cons

    Open MV

  • cons

    and Raspberry Pi

  • cons

    Module 1 and 2 could be shorter

  • cons

    Object Detection needed more explanations

  • cons

    Videos could be shorter

  • cons

    3rd week was too fast

Course Provider

Provider Coursera's Stats at AZClass

Rating Grade: B This is a trending provider perfect for gaining traction and maybe a good option for users who are looking for a reliable source of learning content.

Discussion and Reviews

0.0   (Based on 0 reviews)

Start your review of Computer Vision with Embedded Machine Learning

Quiz

submit successSubmitted Sucessfully

1. What is the prerequisite for this course?

2. What is the main focus of this course?

3. What is the recommended course to take before this one?

4. What is the partnership between Edge Impulse, OpenMV, Seeed Studio, and the TinyML Foundation?

close
part

faq FAQ for Computer Vision Courses

Q1: Does the course offer certificates upon completion?

Yes, this course offers a free certificate. AZ Class have already checked the course certification options for you. Access the class for more details.

Q2: How do I contact your customer support team for more information?

If you have questions about the course content or need help, you can contact us through "Contact Us" at the bottom of the page.

Q3: Can I take this course for free?

Yes, this is a free course offered by Coursera, please click the "go to class" button to access more details.

Q4: How many people have enrolled in this course?

So far, a total of 0 people have participated in this course. The duration of this course is hour(s). Please arrange it according to your own time.

Q5: How Do I Enroll in This Course?

Click the"Go to class" button, then you will arrive at the course detail page.
Watch the video preview to understand the course content.
(Please note that the following steps should be performed on Coursera's official site.)
Find the course description and syllabus for detailed information.
Explore teacher profiles and student reviews.
Add your desired course to your cart.
If you don't have an account yet, sign up while in the cart, and you can start the course immediately.
Once in the cart, select the course you want and click "Enroll."
Coursera may offer a Personal Plan subscription option as well. If the course is part of a subscription, you'll find the option to enroll in the subscription on the course landing page.
If you're looking for additional Computer Vision courses and certifications, our extensive collection at azclass.net will help you.

close

To provide you with the best possible user experience, we use cookies. By clicking 'accept', you consent to the use of cookies in accordance with our Privacy Policy.