Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes

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Overview

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.

What you’ll learn

  • A structured introduction to Data Science and its best practices
  • An introduction to the new Microsoft Azure Machine Learning service, explaining how to effectively build and deploy predictive models as machine learning web services
  • Practical skills such as how to solve typical predictive analytics problems like propensity modeling, churn analysis and product recommendation.
  • An introduction to the following skills: basic Data Science, the Data Mining process, frameworks for solving practical business problems with Machine Learning, and visualization with Power BI
Who this book is for

Data Scientists, Business Analysts, BI Professionals and Developers who are interested in expanding their repertoire of skill applied to machine learning and predictive analytics, as well as anyone interested in an in-depth explanation of the Microsoft Azure Machine Learning service through practical tasks and concrete applications.

The reader is assumed to have basic knowledge of statistics and data analysis, but not deep experience in data science or data mining. Advanced programming skills are not required, although some experience with R programming would prove very useful.

Table of Contents

Part 1: Introducing Data Science and Microsoft Azure machine Learning

1. Introduction to Data Science

2. Introducing Microsoft Azure Machine Learning

3. Integration with R

Part 2: Statistical and Machine Learning Algorithms

4. Introduction to Statistical and Machine Learning Algorithms

Part 3: Practical applications

5. Customer propensity models

6. Building churn models

7. Customer segmentation models

8. Predictive Maintenance

Product Details

ISBN-13: 9781484204450
Publisher: Apress
Publication date: 11/21/2014
Sold by: Barnes & Noble
Format: NOOK Book
Pages: 188
File size: 5 MB

About the Author

Valentine Fontama is a Principal Data Scientist in the Data and Decision Sciences Group (DDSG) at Microsoft, where he leads external consulting engagements that deliver world-class Advanced Analytics solutions to Microsoft’s customers. Val has over 18 years of experience in data science and business. Following a PhD in Artificial Neural Networks, he applied data mining in the environmental science and credit industries. Before Microsoft, Val was a New Technology Consultant at Equifax in London where he pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. He is currently an Affiliate Professor of Data Science at the University of Washington.

In his prior role at Microsoft, Val was a Senior Product Marketing Manager responsible for big data and predictive analytics in cloud and enterprise marketing. In this role, he led product management for Microsoft Azure Machine Learning; HDInsight, the first Hadoop service from Microsoft; Parallel Data Warehouse, Microsoft’s first data warehouse appliance; and three releases of Fast Track Data Warehouse. He also played a key role in defining Microsoft’s strategy and positioning for in-memory computing.

Val holds an M.B.A. in Strategic Management and Marketing from Wharton Business School, a Ph.D. in Neural Networks, a M.Sc. in Computing, and a B.Sc. in Mathematics and Electronics (with First Class Honors). He co-authored the book Introducing Microsoft Azure HDInsight, and has published 11 academic papers with 152 citations by over 227 authors.


Roger Barga is a General Manager and Director of Development at Amazon Web Services. Prior to joining Amazon, Roger was Group Program Manager for the Cloud Machine Learning group in the Cloud & Enterprise division at Microsoft, where his team was responsible for product management of the Azure Machine Learning service. Roger joined Microsoft in 1997 as a Researcher in the Database Group of Microsoft Research, where he directed both systems research and product development eff orts in database, workfl ow, and stream processing systems. He has developed ideas from basic research, through proof of concept prototypes, to incubation efforts in product groups. Prior to joining Microsoft, Roger was a Research Scientist in the Machine Learning Group at the Pacific Northwest National Laboratory where he built and deployed machine learning-based solutions. Roger is also an Affiliate Professor at the University of Washington, where he is a lecturer in the Data Science and Machine Learning programs.

Roger holds a PhD in Computer Science, a M.Sc. in Computer Science with an emphasis on Machine Learning, and a B.Sc. in Mathematics and Computing Science. He has published over 90 peer-reviewed technical papers and book chapters, collaborated with 214 co-authors from 1991 to 2013, with over 700 citations by 1,084 authors.


Wee-Hyong Tok is a Senior Program Manager on the SQL Server team at Microsoft. Wee-Hyong brings over 12 years of database systems experience (with more than six years of data platform experience in industry and six years of academic experience).

Prior to pursuing his PhD, Wee-Hyong was a System Analyst at a large telecommunication company in Singapore, working on marketing decision support systems. Following his PhD in Data Streaming Systems from the National University of Singapore, he joined Microsoft and worked on the SQL Server team. Over the past six years, Wee-Hyong gained extensive experience working with distributed engineering teams from Asia and US, and was responsible for shaping the SSIS Server, bringing it from concept to release in SQL Server 2012. More recently, Wee-Hyong was part of the Azure Data Factory team, a service for orchestrating and managing data transformation and movement.

Wee Hyong holds a Ph.D. in Data Streaming Systems, a M.Sc. in Computing, and a B.Sc. (First Class Honors) in Computer Science, from the National University of Singapore. He has published 21 peer reviewed academic papers and journals. He is a co-author of two books, Introducing Microsoft Azure HDInsight and Microsoft SQL Server 2012 Integration Services.

Table of Contents

Part 1: Introducing Data Science and Microsoft Azure machine Learning

1. Introduction to Data Science

2. Introducing Microsoft Azure Machine Learning

3. Integration with R

Part 2: Statistical and Machine Learning Algorithms

4. Introduction to Statistical and Machine Learning Algorithms

Part 3: Practical applications

5. Customer propensity models

6. Building churn models

7. Customer segmentation models

8. Predictive Maintenance

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