Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

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 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 use on February 18th, 2015. The authors use 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's New in the Second Edition?§§Four new chapters have been added with practical detailed coverage of:§§- Python Integration –new feature announced February 2015§§- Data preparation and feature selection§§- Data visualization with Power BI§§- Recommendation engines§§- Selling your models on Azure Marketplace§§