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Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

By: Ginger Grant,Julio Granados,Guillermo Fernandez,Pau Sempere,Javier Torrenteras,Paco Gonzalez,Tamanaco Francísquez

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Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

By: Ginger Grant,Julio Granados,Guillermo Fernandez,Pau Sempere,Javier Torrenteras,Paco Gonzalez,Tamanaco Francísquez

Prepare for Microsoft Exam 70-774–and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning Develop machine learning models Operationalize and manage Azure Machine Learning Services Use other services for machine learning This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are familiar with Azure data services, machine learning concepts, and common data science processes About the Exam Exam 70-774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data’s behavior; develop models and identify optimal algorithms; train, validate, deploy, manage, and consume Azure Machine Learning Models; and leverage related services and APIs. About Microsoft Certification Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certifi¿cation, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services. See full details at: microsoft.com/learning

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Page Count

566

Publisher

Microsoft Press

Published Date

ISBN 10

013484968X

ISBN 13

9780134849683

Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

By: Ginger Grant,Julio Granados,Guillermo Fernandez,Pau Sempere,Javier Torrenteras,Paco Gonzalez,Tamanaco Francísquez

Write a review Read reviews Take a Quiz Solve Book Puzzle

Exam Ref 70-774 Perform Cloud Data Science with Azure Machine Learning

By: Ginger Grant,Julio Granados,Guillermo Fernandez,Pau Sempere,Javier Torrenteras,Paco Gonzalez,Tamanaco Francísquez

Prepare for Microsoft Exam 70-774–and help demonstrate your real-world mastery of performing key data science activities with Azure Machine Learning services. Designed for experienced IT professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the MCSA level. Focus on the expertise measured by these objectives: Prepare data for analysis in Azure Machine Learning and export from Azure Machine Learning Develop machine learning models Operationalize and manage Azure Machine Learning Services Use other services for machine learning This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are familiar with Azure data services, machine learning concepts, and common data science processes About the Exam Exam 70-774 focuses on skills and knowledge needed to prepare data for analysis with Azure Machine Learning; find key variables describing your data’s behavior; develop models and identify optimal algorithms; train, validate, deploy, manage, and consume Azure Machine Learning Models; and leverage related services and APIs. About Microsoft Certification Passing this exam as well as Exam 70-773: Analyzing Big Data with Microsoft R earns your MCSA: Machine Learning certifi¿cation, demonstrating your expertise in operationalizing Microsoft Azure machine learning and Big Data with R Server and SQL R Services. See full details at: microsoft.com/learning

Average ratings

N/A

5

0%

4

0%

3

0%

2

0%

1

0%

Solve jigsaw puzzle

Age

Page Count

566

Publisher

Microsoft Press

Published Date

ISBN 10

013484968X

ISBN 13

9780134849683