GLOBAL TAT REPOSITORY Amazon has announced that it is to make its machine learning training, previously only available to its internal engineers, available to anyone interested.
30 courses, all self-service and self-managed, are now available, totalling 45 hours across four groups - developers, data scientists, data platform engineers, and business professionals.
Amazon first supported machine learning back in 2015, first in the US, before reaching European servers a few months later.
"There are very few Amazon retail pages, products, fulfillment technologies, stores which haven't been improved through the use of machine learning in one way or another." explains Dr Matt Wood from Amazon Web Services (AWS).
"Each course starts with the fundamentals, and builds on those through real-world examples and labs, allowing developers to explore machine learning through some fun problems we have had to solve at Amazon."
"These include predicting gift wrapping eligibility, optimizing delivery routes, or predicting entertainment award nominations using data from IMDb (an Amazon subsidiary)."
Coursework forms a part of all the modules and is designed to "consolidate best practices, and demonstrates how to get started on a range of AWS machine learning services, including Amazon SageMaker, AWS DeepLens, Amazon Rekognition, Amazon Lex, Amazon Polly, and Amazon Comprehend."
Completion of the relevant modules can lead to a new AWS Certification for Machine Learning, subject to a final exam, which is being offered at half price for a limited period.
The courses themselves are free, with end users only responsible for the services they use during the training, and that afforementioned exam. If you're at AWS re:Invent, you can even sit the exam this week, if you're ready.
Courses include "Demystifying Machine Learning", "Exploring the Machine Learning Toolset", "Seeing Clearly: Computer Vision Theory", and "ML Building Blocks".
But don't even think of cheating, a recent report showed that the individual quirks of programmer's individual styles can reveal their identities. With a bit of help from machine learning, obviously. μ
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