Create and operationalize a predictive model using Microsoft Azure Machine Learning.
–Perform the typical steps involved in building a predictive analytics solution such as data ingestion, data cleansing, data exploration, feature engineering, model selection and evaluation of model results
–learn how to use machine learning with big data scenarios using tools like Hadoop and SQL Server to process and work with such data.
Fahad Shah is a Data Scientist in the Azure Machine Learning team at Microsoft. Fahad has lead the initiatives for fraud detection, customer churn, product demand forecasting, predictive maintenance and cloud data science process. Previously, Fahad worked on online user behavior modeling and root cause detection from logs whilst at Bing. He has also worked on exploring social integration with search and comes with years of teaching and industry experience before joining Microsoft. Fahad obtained his Ph.D in CS (topic: Social Network mining and Community analysis) from University of Central Florida.
Fidan Boylu holds a Ph.D in Decision Sciences and has 10+ years of technical experience on data mining and business intelligence. She is a former professor at the University of Connecticut where she conducted research and taught courses on data mining theory and its business applications. She has a number of academic publications ranging in the areas of machine learning and optimization with applications such as credit scoring and recommendation systems. She is currently working as a senior data scientist at Microsoft and responsible for successful delivery of end to end advanced analytics solutions. She has worked on a number of projects in multiple domains such as predictive maintenance and fraud detection