Predictive Analytics with Microsoft Azure Machine Learning, 2/e (Paperback)
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:
- Python Integration – a new feature announced February 2015
- Data preparation and feature selection
- Data visualization with Power BI
- Recommendation engines
- Selling your models on Azure Marketplace
What youll 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
- Practical skills such as how to solve typical predictive analytics problems like propensity modeling, churn analysis, product recommendation, and visualization with Power BI
- A practical way to sell your own predictive models on the Azure Marketplace
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. Data Preparation
4. Integration with R
Part 2: Statistical and Machine Learning Algorithms
5. Integration with Python
Part 3: Practical applications
6. Introduction to Statistical and Machine Learning Algorithms
7. Building Customer Propensity Models
8. Visualizing Your Models with Power BI
9. Building Churn Models
10. Customer Segmentation Models
11. Building Predictive Maintenance Models
12. Recommendation Systems
13. Consuming and Publishing Models on Azure Marketplace
14. Cortana Analytics