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Hands-On Machine Learning with Azure

Hands-On Machine Learning with Azure

By : Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy
2.7 (3)
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Hands-On Machine Learning with Azure

Hands-On Machine Learning with Azure

2.7 (3)
By: Abraham, Parashar Shah, Jen Stirrup, Lauri Lehman, Anindita Basak, Murphy

Overview of this book

Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models.
Table of Contents (14 chapters)
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Machine Learning Server

Microsoft ML Server and its capabilities in SQL Server and HDInsight are the subject of this chapter. In addition, the chapter will provide a walk-through on ML Server's use in order to demonstrate optimal situations in which to use it and how to deploy a solution with it.

Classified algorithms are supervised learning algorithms, which means that they make predictions based on a set of examples.

Often, it is useful to use data to predict a category, and this is known as classification. Take, for example, Andrew Ng's work on the classification of YouTube content as a cat video, or a video of something that is not a cat. As in the famous work by Andrew Ng, when there are only two choices, it is called two-class or binomial classification. When there are more categories, this problem is known as multi-class classification. Multi-class classification...

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