Predictive Analytics with Microsoft Azure Machine Learning

Abstract

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. Whats New in the Second Edition? Five new chapters have been added with practical detailed coverage of: Python Integration a new feature announced February 2015Data preparation and feature selection Data visualization with Power BIRecommendation 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 BIA 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.