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Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity

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Machine learning (ML) is one of the fastest growing areas in technology and a highly sought-after skill set in today’s job market. The growth of artificial intelligence is expected to create 58 million net new jobs in the next few years, according to the World Economic Forum [1]. However, according to the Tencent Research Institute, it’s estimated that currently there are only 300,000 AI engineers worldwide, while millions are needed [2].

To help you advance your AI/ML skills with hands-on and engaging learning, AWS is announcing the AWS Machine Learning Scholarship Program, built in collaboration with Udacity.

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity 1

What is the AWS Machine Learning Scholarship Program?

AWS and Udacity are collaborating to educate developers of all skill levels to get started with ML. This scholarship is open to all applicants interested in expanding their AWS ML expertise.

In this dual scholarship program, all eligible students who complete the free AWS Machine Learning Foundations course earn a course completion certificate from Udacity and have the opportunity to take a high-bar knowledge test.

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity 2

Based on the scores from this test, the top 325 developers will receive full scholarships to the Machine Learning Engineer Nanodegree program. The Udacity Nanodegree program is a unique online educational offering designed to bridge the gap between skills and career goals.

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity 3

How does the scholarship program work?

The scholarship program begins May 19, 2020, and runs through July 31, 2020. You can join the program at any point during this period and immediately enroll into Udacity’s AWS Machine Learning Foundations course for free. You can get started with ML basics, techniques, and skills—including programming practices—and learn more about AWS ML. To advance your ML learning, register now.

The AWS Machine Learning Foundations course is intended to help developers of all skill levels get started with ML. In the foundational course curriculum, you build your knowledge with software and engineering practices, like object oriented programming (OOP), writing clean code, testing code, scikit-learn, putting code on PyPI, and more. In addition to building basic software engineering skills, you also build an intermediate knowledge of ML skills, such as supervised and unsupervised learning. Furthermore, you learn about flagship AWS educational AI devices, like AWS DeepRacer, AWS DeepLens, and AWS DeepComposer.

After you complete the foundational course, all eligible students can take a high-bar knowledge test. The top scorers qualify for one of 325 full scholarships to the Udacity Machine Learning Engineer Nanodegree program, sponsored by Udacity.

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity 4

Next steps

To get notified about the scholarship program and enrollment dates, register now. For a more information, see AWS Machine Learning Scholarship Program.

Developers, get started with ML! The scholarship program starts May 19, 2020.


About the Author

Advance your career with a scholarship to the AWS Machine Learning Engineer Nanodegree program from Udacity 5Tara Shankar Jana is a Senior Product Marketing Manager for AWS Machine Learning. Currently he is working on building unique and scalable educational offerings for the aspiring ML developer communities- to help them expand their skills on ML. Outside of work he loves reading books, travelling and spending time with his family.

 

 

 


[1] Artificial Intelligence To Create 58 Million New Jobs By 2022, Says Report (Forbes)

[2] Tencent says there are only 300,000 AI engineers worldwide, but millions are needed (The Verge)



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