Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Hands-On Machine Learning with Azure

You're reading from   Hands-On Machine Learning with Azure Build powerful models with cognitive machine learning and artificial intelligence

Arrow left icon
Product type Paperback
Published in Oct 2018
Publisher Packt
ISBN-13 9781789131956
Length 340 pages
Edition 1st Edition
Languages
Tools
Arrow right icon
Authors (6):
Arrow left icon
Jen Stirrup Jen Stirrup
Author Profile Icon Jen Stirrup
Jen Stirrup
Ryan Murphy Ryan Murphy
Author Profile Icon Ryan Murphy
Ryan Murphy
Anindita Basak Anindita Basak
Author Profile Icon Anindita Basak
Anindita Basak
Thomas K Abraham Thomas K Abraham
Author Profile Icon Thomas K Abraham
Thomas K Abraham
Parashar Shah Parashar Shah
Author Profile Icon Parashar Shah
Parashar Shah
Lauri Lehman Lauri Lehman
Author Profile Icon Lauri Lehman
Lauri Lehman
+2 more Show less
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. AI Cloud Foundations 2. Data Science Process FREE CHAPTER 3. Cognitive Services 4. Bot Framework 5. Azure Machine Learning Studio 6. Scalable Computing for Data Science 7. Machine Learning Server 8. HDInsight 9. Machine Learning with Spark 10. Building Deep Learning Solutions 11. Integration with Other Azure Services 12. End-to-End Machine Learning 13. Other Books You May Enjoy

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...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image