Predictive Analytics with Microsoft Azure Machine Learning, Second Edition is a practical tutorial introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book provides a thorough overview of the Microsoft Azure Machine Learning service released for general availability on February 18th, 2015 with practical guidance for building recommenders, propensity models, and churn and predictive maintenance models.
The authors use task oriented descriptions and concrete end-to-end examples to ensure that the reader can immediately begin using this new service. The book describes all aspects of the service from data ingress to applying machine learning, evaluating the models, and deploying them as web services.
Learn how you can quickly build and deploy sophisticated predictive models with the new Azure Machine Learning from Microsoft.
What’s New in the Second Edition?
Five new chapters have been added with practical detailed coverage of:
Table of Contents
- Contents at a Glance
- About the Authors
- About the Techincal reviewers
Part I: 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
- Introducing the Gallery
- Five Easy Steps to Creating a Training Experiment
- Deploying Your Model in Production
- Chapter 3: Data Preparation
- Chapter 4: Integration with R
- Chapter 5: Integration with Python
- Chapter 1: Introduction to Data Science
- Part II: Statistical and Machine Learning Algorithms
Part III: Practical Applications
- Chapter 7: Building Customer Propensity Models
Chapter 8: Visualizing Your Models with Power BI
- Introducing Power BI
- Three Approaches for Visualizing with Power BI
- Scoring Your Data in Azure Machine Learning and Visualizing in Excel
- Scoring and Visualizing Your Data in Excel
- Scoring Your Data in Azure Machine Learning and Visualizing in powerbi.com
- Chapter 9: Building Churn Models
Chapter 10: Customer Segmentation Models
- Customer Segmentation Models in a Nutshell
- Building and Deploying Your First K-Means Clustering Model
- Customer Segmentation of Wholesale Customers
Chapter 11: Building Predictive Maintenance Models
- Predictive Maintenance Scenarios
- The Business Problem
- Data Acquisition and Preparation
- Training the Model
- Model Testing and Validation
- Model Performance
- Techniques for Improving the Model
- Model Deployment
- Chapter 12: Recommendation Systems
Chapter 13: Consuming and Publishing Models on Azure Marketplace
- What Are Machine Learning APIs?
- How to Use an API from Azure Marketplace
- Publishing Your Own Models in Azure Marketplace
- Creating and Publishing a Web Service for Your Machine Learning Model
- Obtaining the API Key and the Details of the OData Endpoint
- Publishing Your Model as an API in Azure Marketplace
- Chapter 14: Cortana Analytics
- Title: Predictive Analytics with Microsoft Azure Machine Learning, Second Edition
- Release date: August 2015
- Publisher(s): Apress
- ISBN: 9781484212004