New content in the Microsoft Press Guided Tours app: Azure Machine Learning


NOTE: The Microsoft Press Guided Tours app has been discontinued and is no longer available in the Windows Store. If you have already installed the app, you can continue to use it for as long as you like. All the tours will remain available for download from within the app.

The free Microsoft Press Guided Tours app is newly updated on Windows Store! The newest tour on our growing list is “Microsoft Azure Essentials: Azure Machine Learning.”

In this Windows 8.1 app, Microsoft Press authors provide insightful coverage of new and evolving Microsoft technologies. You can use the app to explore technical topics in powerful new ways, and you can mark up content in multiple ways so that it’s more useful to you.

The following five free guided tours are included in our app – and more are coming soon!

  • Building cloud apps with Microsoft Azure (including best practices for DevOps, data storage, high availability, and more), by Scott Guthrie, Mark Simms, Tom Dykstra, Rick Anderson, and Mike Wasson
  • Programming Windows Store apps with HTML, CSS, and JavaScript, by author Kraig Brockschmidt
  • Using Microsoft Azure HDInsight, by Avkash Chauhan, Valentine Fontama, Michele Hart, Wee Hyong Tok, and Buck Woody
  • Microsoft Azure Essentials: Fundamentals of Azure, by Michael S. Collier and Robin E. Shahan
  • Microsoft Azure Essentials: Azure Machine Learning, by Jeff Barnes

Download from Windows Store

Introduction

Microsoft Azure Machine Learning (ML) is a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. Azure ML Studio provides rich functionality to support many end-to-end workflow scenarios for constructing predictive models, from easy access to common data sources, rich data exploration and visualization tools, application of popular ML algorithms, and powerful model evaluation, experimentation, and web publication tooling.

This guided tour will present an overview of modern data science theory and principles, the associated workflow, and then cover some of the more common machine learning algorithms in use today. We will build a variety of predictive analytics models using real world data, evaluate several different machine learning algorithms and modeling strategies, and then deploy the finished models as machine learning web service on Azure within a matter of minutes. The tour will also expand on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services.

The scenarios and end-to-end examples in this guided tour are intended to provide sufficient information for you to quickly begin leveraging the capabilities of Azure ML Studio and then easily extend the sample scenarios to create your own powerful predictive analytic experiments. The tour wraps up by providing details on how to apply “continuous learning” techniques to programmatically “retrain” Azure ML predictive models without any human intervention.

Who should take this tour

This guided tour focuses on providing essential information about the theory and application of data science principles and techniques and their applications within the context of Azure Machine Learning Studio. The tour is targeted towards both data science hobbyists and veterans, along with developers and IT professionals who are new to machine learning and cloud computing. Azure ML makes it just as approachable for a novice as a seasoned data scientist, helping you quickly be productive and on your way towards creating and testing machine learning solutions.

Detailed, step-by-step examples and demonstrations are included to help the reader understand how to get started with each of the key predictive analytic algorithms in use today and their corresponding implementations in Azure ML Studio. This material is useful not only for those who have no prior experience with Azure Machine Learning, but also for those who are experienced in the field of data science. In all cases, the end-to-end demos help reinforce the machine learning concepts with concrete examples and real-life scenarios. The sections do build on each other to some extent; however, there is no requirement that you perform the hands-on demonstrations from previous sections to understand any particular section.

Assumptions
We expect that you have at least a minimal understanding of cloud computing concepts and basic web services. There are no specific skills required overall for getting the most out of this guided tour, but having some knowledge of the topic of each section will help you gain a deeper understanding. For example, the section on creating Azure ML client and server applications will make more sense if you have some understanding of web development skills. Azure Machine Learning Studio automatically generates code samples to consume predictive analytic web services in C#, Python, and R for each Azure ML experiment. A working knowledge of one of these languages is helpful but not necessary.

This tour might not be for you if…
This guided tour might not be for you if you are looking for an in-depth discussion of the deeper mathematical and statistical theories behind the data science algorithms covered in the tour. The goal was to convey the core concepts and implementation details of Azure Machine Learning Studio to the widest audience possible—who may not have a deep background in mathematics and statistics.

Organization of this tour

This guided tour explores the background, theory, and practical applications of today’s modern data science algorithms using Azure Machine Learning Studio. Azure ML predictive models are then generated, evaluated, and published as web services for consumption and testing by a wide variety of clients to complete the feedback loop.

The topics explored in this guided tour include:

    Section 1, “Introduction to the science of data,” Learning represents a critical step forward in democratizing data science by making available a fully-managed cloud service for building predictive analytics solutions.

    • Section 2, “Getting started with Azure Machine Learning,” covers the basic concepts behind the science and methodology of predictive analytics.

    • Section 3, “Using Azure Machine Learning Studio,” explores the basic fundamentals of Azure Machine Learning Studio and helps you get started on your path towards data science greatness.

    • Section 4, “Creating Azure Machine Learning client and server applications,” expands on a working Azure Machine Learning predictive model and explores the types of client and server applications that you can create to consume Azure Machine Learning web services.

    • Section 5, “Regression analytics,” takes a deeper look at some of the more advanced machine learning algorithms that are exposed in Azure ML Studio.

    • Section 6, “Cluster analytics,” explores scenarios where the machine conducts its own analysis on the dataset, determines relationships, infers logical groupings, and generally attempts to make sense of chaos by literally determining the forests from the trees.

    • Section 7, “The Azure ML Matchbox recommender,” explains one of the most powerful and pervasive implementations of predictive analytics in use today on the web today and how it is crucial to success in many consumer industries.

    • Section 8, “Retraining Azure ML models,” explores the mechanisms for incorporating “continuous learning” into the workflow for our predictive models.

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