AWS Computer Vision: Getting Started with GluonCV by Amazon Web Services via Coursera: Fee, Duration, How to Apply

Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

The online course in AWS Computer Vision: Getting Started with GluonCV by Coursera is spread out over six weeks, wherein a new module is picked up every week, to focus on computer vision’s various aspects with GluonCV. The programme starts by discussing the basic concepts of computer vision and progresses by providing an overview of machine learning, using the GluonCV toolkit and Apache MXNet to build and train CV models, discussing deep learning concepts, and artificial neural networks. In the last week, the candidates submit a final project where they apply their learnings and visualize the output.

The AWS Computer Vision: Getting Started with GluonCV online course is offered by Amazon Web Services on Coursera, and thus, comprehensively covers the various AWS services like Amazon SageMaker, AWS Deep Learning, Amazon Rekognition and many more. The curriculum provides content through pre-recorded videos, readings, practice quizzes, and flexible deadlines, to help candidates learn at their own pace.

Furthermore, the AWS Computer Vision: Getting Started with GluonCV online programme provides financial aid as well. It will equip the candidates with the knowledge on how to build a computer vision model, how to set up different services, their benefits and limitations.

Course and certificate fees

Type of course

Free

The online course in AWS Computer Vision: Getting Started with GluonCV is available for free using the audit mode on Coursera. No fee payment is required. For getting the graded assignment and certificate you have to pay an amount of Rs. 2,152.

certificate availability

Yes

certificate providing authority

Coursera

certificate fees

₹2,216

Who it is for

Machine learning engineer

What you will learn

Knowledge of deep learning Knowledge of aws technology

Upon successful completion of the AWS Computer Vision: Getting Started with GluonCV programme, candidates will have gained knowledge of the following:

The benefits of Apache MXNet
Selection of appropriate AWS services such as Amazon Sagemaker and Amazon Rekognition, as per the task
What tasks can GluonCV solve
How to set up GluonCV and MXNet
MXNet’s easy-to-use, high-level API
Fundamentals of GluonCV, constructing datasets in GluonCV, understanding when to use different Gluon blocks, combining those blocks into finished models
How to combine several building blocks of neural networks to form complete computer vision models and training them efficiently
Differences between deep learning containers and AWS deep learning AMIs
Selecting the correct, pre-trained GluonCV models, applying them to datasets, and visualizing their outputs

The syllabus

Introduction to Computer Vision

Meet the instructors
Computer vision overview
Computer vision tasks
GluonCV model zoo
GluonCV
Imperative vs symbolic
Apache MXNet
Weekly summary
Weekly overview

Machine Learning on AWS

Weekly overview
AWS Machine Learning Stack
Amazon SageMaker
Amazon SageMaker: demonstration
Amazon Rekognition
Amazon Rekognition: Demonstration
AWS Deep Learning Containers
AWS Deep Learning Containers: Demonstration
AWS Deep Learning AMIs
AWS Deep Learning AMIs: Demonstration
Weekly summary

Using GluonCV Models

Weekly overview
Setting up GluonCV
Image Classification
Image Classification: step by step demonstration
Image Classification: one line demonstration
Neural Network Essentials: convolution and max-pooling
Neural Network Essentials: fully connected
Object detection
Object detection: step by step demonstration
Image segmentation
Image segmentation: step by step demonstration
Weekly summary

Gluon Fundamentals

Weekly overview
Metrics
Losses
Metrics vs losses
MXNet NDArrays
Common NDArray operations
Gluon blocks
MXNet vs NumPy NDArray
Initialization of Gluon blocks
Custom Gluon blocks
Sequential Gluon blocks
Visualization of Gluon blocks
Weekly summary

Gluon Fundamentals (Continued)

Weekly overview
Automatic differentiation
MXNet optimizers
Data in machine learning
Gluon trainers
MXNet autograd
Gluon data loaders
Gluon datasets
Gluon transformations
Neural network evaluation
Neural network training
Weekly summary

Final Project

Counting people in images
Course summary

Admission details

To enrol in the AWS Computer Vision: Getting Started with GluonCV programme by Coursera:

Visit the course page.
Click on the ‘Enroll for Free’ button that is under the course title on the page.
Sign up by entering your name, password, and Email ID. If you already have an account, enter those details to proceed.
After you enter your payment details, your 7-day free trial will get activated, at the end of which, the payment will be deducted if you want to continue. You can cancel the free trial any time.
You can opt to audit the course for free.

Filling the form

No application form has to be filled by candidates for the AWS Computer Vision: Getting Started with GluonCV programme by Coursera. You can register for this course using your Coursera account. To confirm the enrolment, candidates have to successfully make the payment via their preferred method of transaction.

Scholarship Details

For the AWS Computer Vision: Getting Started with GluonCV online course by Coursera, students who cannot afford to pay the total fees can opt for ‘Financial Aid’. This button is present at the top of the page, under the course title.

To apply for financial aid, candidates must fill an application form, and Coursera will inform them about the approval.

How it helps

The AWS Computer Vision: Getting Started with GluonCV programme is beneficial for those who want to advance in machine learning and computer vision. After completing this course, the candidates will be able to train a CV model using the GluonCV and MXNet toolkits. The training is provided by top-rated instructors at Coursera, and the candidates will also create a final project under their guidance.

The AWS Computer Vision: Getting Started with GluonCV course curriculum provides an all-round learning experience, complete with hands-on exercise guides, demonstrations, practice quizzes for every module, graded assignments, and feedback to help them consolidate their knowledge. Moreover, students get a professional certificate that can be shared across their LinkedIn profile, printed resumes and CVs.

FAQs

What kind of job opportunities can I get after this course?

After this course, you can get job titles such as Computer Vision Engineer, Computer Vision Systems specialist, CV Software Engineer and more.

What are the benefits of availing this course in Coursera Plus?

With Coursera Plus, you can get not only this but over 3000 courses along with guided projects and specializations. You can also earn multiple professional certifications and learn from the most prestigious institutions like Johns Hopkins University, Duke University, Google, etc.

Will I get access to assignments and lectures in the audit mode?

You will get access to most of the course materials (readings and videos) in the free audit mode, except for the graded assignments and completion certificate.

What kinds of tests are conducted?

The curriculum for the AWS Computer Vision: Getting Started with GluonCV certification course provides module quizzes and a final project to help you test your learning.

How long does it take to finish the course?

The course consists of six modules, one for each week, with different stipulated time limits. Since this is a self-paced course, you can set flexible deadlines in accordance with your schedule and finish the course at your own pace.