Predictive Analytics with Microsoft Azure Machine Learning 2nd Edition

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.

• 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.-