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.
Table of contents
- Contents at a Glance
- About the Authors
Part 1: Introducing Data Science and Microsoft Azure Machine Learning
Chapter 1: Introduction to Data Science
- What Is Data Science?
- Analytics Spectrum
- Why Does It Matter and Why Now?
- Common Data Science Techniques
- Cutting Edge of Data Science
Chapter 2: Introducing Microsoft Azure Machine Learning
- Hello, Machine Learning Studio!
- Components of an Experiment
- Five Easy Steps to Creating an Experiment
- Deploying Your Model in Production
- Chapter 3: Integration with R
- Chapter 1: Introduction to Data Science
- Part 2: Statistical and Machine Learning Algorithms
Part 3: Practical Applications
- Chapter 5: Building Customer Propensity Models
- Chapter 6: Building Churn Models
Chapter 7: Customer Segmentation Models
- Customer Segmentation Models in a Nutshell
- Building and Deploying Your First K-Means Clustering Model
- Customer Segmentation of Wholesale Customers
- Chapter 8: Building Predictive Maintenance Models
- Title: Predictive Analytics with Microsoft Azure Machine Learning: Build and Deploy Actionable Solutions in Minutes
- Release date: November 2014
- Publisher(s): Apress
- ISBN: 9781484204450
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