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Self-service AI with Power BI Deskto...
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Ehrenmueller-Jensen, Markus.
Self-service AI with Power BI Desktopmachine learning insights for business /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Self-service AI with Power BI Desktopby Markus Ehrenmueller-Jensen.
Reminder of title:
machine learning insights for business /
Author:
Ehrenmueller-Jensen, Markus.
Published:
Berkeley, CA :Apress :2020.
Description:
xxiii, 344 p. :ill., digital ;24 cm.
Contained By:
Springer Nature eBook
Subject:
BusinessData processing.
Online resource:
https://doi.org/10.1007/978-1-4842-6231-3
ISBN:
9781484262313$q(electronic bk.)
Self-service AI with Power BI Desktopmachine learning insights for business /
Ehrenmueller-Jensen, Markus.
Self-service AI with Power BI Desktop
machine learning insights for business /[electronic resource] :by Markus Ehrenmueller-Jensen. - Berkeley, CA :Apress :2020. - xxiii, 344 p. :ill., digital ;24 cm.
1. Asking Questions in Natural Language -- 2. The Insights Feature -- 3. Discovering Key Influencers -- 4. Drill-Down and Decomposing Hierarchies -- 5. Adding Smart Visualizations -- 6. Experimenting with Scenarios -- 7. Characterizing a Dataset -- 8. Creating Columns from Example -- 9. Executing R and Python Visualizations -- 10. Transforming Data with R and Python -- 11. Execute Machine Learning Models in the Azure Cloud.
ISBN: 9781484262313$q(electronic bk.)
Standard No.: 10.1007/978-1-4842-6231-3doiSubjects--Uniform Titles:
Microsoft .NET Framework.
Subjects--Topical Terms:
199085
Business
--Data processing.
LC Class. No.: HF5548.2 / .E374 2020
Dewey Class. No.: 650.285631
Self-service AI with Power BI Desktopmachine learning insights for business /
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1. Asking Questions in Natural Language -- 2. The Insights Feature -- 3. Discovering Key Influencers -- 4. Drill-Down and Decomposing Hierarchies -- 5. Adding Smart Visualizations -- 6. Experimenting with Scenarios -- 7. Characterizing a Dataset -- 8. Creating Columns from Example -- 9. Executing R and Python Visualizations -- 10. Transforming Data with R and Python -- 11. Execute Machine Learning Models in the Azure Cloud.
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EB HF5548.2 .E33 2020 2020
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https://doi.org/10.1007/978-1-4842-6231-3
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