Microsoft Azure Essentials: Azure Machine Learning eBook

Hello everyone, you can download Microsoft Azure Essentials Azure Machine Learning eBook and yes, it is 100% free thanks to the author Jeff Barnes and Microsoft for providing this much needed book for free especially to those of you who are new to Machine Learning with Azure as your platform/foundation. We have three formats for you, you can get this eBook via PDF, ePub and Mobi.

If you want to quickly get started with using Azure for machine learning, this book is a must-have. It’s suitable for learners at any level, from beginner to expert. You’ll learn something new or refresh your memory on important topics. I recently read this eBook and found it so useful that I bookmarked it as a reference for future use as I continue my own little personal dive into artificial intelligence. You can also choose to read our comprehensive and quiet detailed getting started with Azure Machine Learning guide.

Table of Contents

What does Microsoft Azure Essentials Azure Machine Learning Cover?

This third ebook in the series introduces Microsoft Azure Machine Learning, a platform that allows developers to build and deploy predictive analytics models as cloud web services. It covers fundamental data science concepts, the data science workflow, and common machine learning algorithms. Using real-world data, the ebook demonstrates how to build and evaluate various predictive models, and then quickly deploy them as web services on Azure. It also showcases how to consume Azure Machine Learning web services in various client and server applications by expanding on a working example of an Azure Machine Learning predictive model.

Chapter Previews of this book:

Chapter 1, “Introduction to the science of data,” introduces Azure Machine Learning as a service for building predictive analytics solutions.
Chapter 2, “Getting started with Azure Machine Learning,” covers the basics of predictive analytics.
Chapter 3, “Using Azure ML Studio,” explains the fundamentals of Azure Machine Learning Studio.
Chapter 4, “Creating Azure ML client and server applications,” demonstrates how to consume Azure Machine Learning web services in various client and server applications using a working example.
Chapter 5, “Regression analytics,” examines advanced machine learning algorithms in Azure ML Studio.
Chapter 6, “Cluster analytics,” explores how the machine can analyze a dataset, identify relationships, and group data to make sense of it.
Chapter 7, “The Azure ML Matchbox recommender,” discusses a common application of predictive analytics in consumer industries.
Chapter 8, “Retraining Azure ML models,” covers the process of continuously learning and improving predictive models.