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

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