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2014 | Buch

Predictive Analytics with Microsoft Azure Machine Learning

Build and Deploy Actionable Solutions in Minutes

verfasst von: Roger Barga, Valentine Fontama, Wee Hyong Tok

Verlag: Apress

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Über dieses Buch

Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis.

The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models.

The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter.

The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

Inhaltsverzeichnis

Frontmatter

Introducing Data Science and Microsoft Azure Machine Learning

Frontmatter
Chapter 1. Introduction to Data Science
Abstract
So what is data science and why is it so topical? Is it just another fad that will fade away after the hype? We will start with a simple introduction to data science, defining what it is, why it matters, and why now. This chapter highlights the data science process with guidelines and best practices. It introduces some of the most commonly used techniques and algorithms in data science. And it explores ensemble models, a key technology on the cutting edge of data science.
Roger Barga, Valentine Fontama, Wee Hyong Tok
Chapter 2. Introducing Microsoft Azure Machine Learning
Abstract
■■■
Roger Barga, Valentine Fontama, Wee Hyong Tok
Chapter 3. Integration with R
Abstract
This chapter will introduce R and show how it is integrated with Microsoft Azure Machine Learning. Through simple examples, you will learn how to write and run your own R code when working with Azure Machine Learning. You will also learn the R packages supported by Azure Machine Learning, and how you can use them in the Azure Machine Learning Studio (ML Studio).
Roger Barga, Valentine Fontama, Wee Hyong Tok

Statistical and Machine Learning Algorithms

Frontmatter
Chapter 4. Introduction to Statistical and Machine Learning Algorithms
Abstract
This chapter will serve as a reference for some of the most commonly used algorithms in Microsoft Azure Machine Learning. We will provide a brief introduction to algorithms such as linear and logistic regression, k-means for clustering, decision trees, decision forests (random forests, boosted decision trees, and Gemini), neural networks, support vector machines, and Bayes point machines.
Roger Barga, Valentine Fontama, Wee Hyong Tok

Practical Applications

Frontmatter
Chapter 5. Building Customer Propensity Models
Abstract
This chapter will provide a practical guide for building machine learning models. It focuses on buyer propensity models, showing how to apply the data science process to this business problem. Through a step-by-step guide, this chapter will explain how to apply key concepts and leverage the capabilities of Microsoft Azure Machine Learning for propensity modeling.
Roger Barga, Valentine Fontama, Wee Hyong Tok
Chapter 6. Building Churn Models
Abstract
In this chapter, we reveal the secrets of building customer churn models, which are in very high demand. Many industries use churn analysis as a means of reducing customer attrition. This chapter will show a holistic view of building customer churn models in Microsoft Azure Machine Learning.
Roger Barga, Valentine Fontama, Wee Hyong Tok
Chapter 7. Customer Segmentation Models
Abstract
In this chapter, you will learn how to build customer segmentation models in Microsoft Azure Machine Learning. Using a practical example, we present a step-by-step guide on using Microsoft Azure Machine Learning to easily build segmentation models using k-means clustering. After the models have been built, you will learn how to perform validation and deploy it in production.
Roger Barga, Valentine Fontama, Wee Hyong Tok
Chapter 8. Building Predictive Maintenance Models
Abstract
The leading manufacturers are now investing in predictive maintenance, which holds the potential to reduce cost yet increase margin and customer satisfaction. Though traditional techniques such as statistics and manufacturing have helped, the industry is still plagued by serious quality issues and the high cost of business disruption when components fail.  Advances in machine learning offer a unique opportunity to improve customer satisfaction and reduce service downtime.   This chapter shows how to build models for predictive maintenance using Microsoft Azure Machine Learning. Through examples we will demonstrate how you can use Microsoft Azure Machine Learning to build, validate, and deploy a predictive model for predictive maintenance.
Roger Barga, Valentine Fontama, Wee Hyong Tok
Backmatter
Metadaten
Titel
Predictive Analytics with Microsoft Azure Machine Learning
verfasst von
Roger Barga
Valentine Fontama
Wee Hyong Tok
Copyright-Jahr
2014
Verlag
Apress
Electronic ISBN
978-1-4842-0445-0
Print ISBN
978-1-4842-0446-7
DOI
https://doi.org/10.1007/978-1-4842-0445-0

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