Amazon Machine Learning: New Course

Tame the data beast with Amazon Machine Learning

With the explosion of web-based computing of one sort or another, just about every one of the millions of transactions and connections taking place will generate some kind of record.

The odds are that one way or another, you’ve got access to data that’s just waiting to be explored and, where necessary, exploited. Looking to build a smart recommendation engine into your web site search tool? Would you like your customer email targeting to be razor sharp accurate?

How about saving time (and money) by automatically directing incoming communications precisely where they’ll get the quickest response? The good news is that machine learning can make all of that happen, and the possible applications are limited only by your imagination.

The better news is that Cloud-based tools like Amazon Machine Learning, Azure Machine Learning, and the Google Prediction API, have brought machine learning within reach of just about anyone sufficiently motivated to learn how it’s done.

And the best news is that Cloud Academy has just released a really comprehensive course on Amazon Machine Learning from our own data and code guru, James Counts.

James will guide you through the whole process, starting with the basic principles and range of machine learning, and leading all the way up to a practical demonstration of generating both batch and real-time predictions.
Along the way, you’ll learn how to accurately frame your problem, format and import your data, create, evaluate, and fine-tune data models, and interpret your predictions.

Learn how your machine learns

Cloud Academy’s new course is perfectly designed to give you everything you’ll need to make the move to mastering your own big data. Give it a shot!

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Written by

David Clinton

A Linux system administrator with twenty years' experience as a high school teacher, David has been around the industry long enough to have witnessed decades of technology trend predictions; most of them turning out to be dead wrong.

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