Predictive Analytics with Microsoft Azure Machine Learning : Build and Deploy Actionable Solutions in Minutes

Tok, Wee Hyong, Barga, Roger, Fontama, Valentine

ISBN 10: 1484204468 / ISBN 13: 9781484204467

Edité par Apress L. P., 2014

A propos de cet article

Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

N° de réf. du vendeur 39995162-20

Détails bibliographiques

• Titre: Predictive Analytics with Microsoft Azure Machine Learning : Build and Deploy Actionable Solutions in Minutes
• Éditeur: Apress L. P.
• Date de parution: 2014
• Langue: anglais
• ISBN à 10 chiffres: 1484204468
• ISBN à 13 chiffres: 9781484204467
• Reliure: Couverture souple
• État de l'article: Very Good
• Poids: 6,182 livres

A propos de ce titre

Synopsis

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.

À propos de l?auteur

Valentine Fontama is a Principal Data Scientist in the Data and Decision Sciences Group (DDSG) at Microsoft, where he leads external consulting engagements that deliver world-class Advanced Analytics solutions to Microsoft’s customers. Val has over 18 years of experience in data science and business. Following a PhD in Artificial Neural Networks, he applied data mining in the environmental science and credit industries. Before Microsoft, Val was a New Technology Consultant at Equifax in London where he pioneered the application of data mining to risk assessment and marketing in the consumer credit industry. He is currently an Affiliate Professor of Data Science at the University of Washington. In his prior role at Microsoft, Val was a Senior Product Marketing Manager responsible for big data and predictive analytics in cloud and enterprise marketing. In this role, he led product management for Microsoft Azure Machine Learning; HDInsight, the first Hadoop service from Microsoft; Parallel Data Warehouse, Microsoft’s first data warehouse appliance; and three releases of Fast Track Data Warehouse. He also played a key role in defining Microsoft’s strategy and positioning for in-memory computing.Val holds an M.B.A. in Strategic Management and Marketing from Wharton Business School, a Ph.D. in Neural Networks, a M.Sc. in Computing, and a B.Sc. in Mathematics and Electronics (with First Class Honors). He co-authored the book Introducing Microsoft Azure HDInsight, and has published 11 academic papers with 152 citations by over 227 authors.