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Power BI Machine Learning and OpenAI

You're reading from Power BI Machine Learning and OpenAI

Product type Book

Published in May 2023

Publisher Packt

ISBN-13 9781837636150

Pages 308 pages

Edition 1st Edition

Languages

Concepts

Author (1):

Greg Beaumont

Table of Contents (21) Chapters

Preface

Part 1: Data Exploration and Preparation

Chapter 1: Requirements, Data Modeling, and Planning

Chapter 2: Preparing and Ingesting Data with Power Query

Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model

Chapter 4: Model Data for Machine Learning in Power BI

Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service

Chapter 5: Discovering Features Using Analytics and AI Visuals

Chapter 6: Discovering New Features Using R and Python Visuals

Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service

Part 3: Machine Learning in Power BI

Chapter 8: Building Machine Learning Models with Power BI

Chapter 9: Evaluating Trained and Tested ML Models

Chapter 10: Iterating Power BI ML models

Chapter 11: Applying Power BI ML Models

Part 4: Integrating OpenAI with Power BI

Chapter 12: Use Cases for OpenAI

Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows

Chapter 14: Project Review and Looking Forward

Index

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Summary

In this chapter, you discovered new features and added them to pages of your Power BI report. You also used the Power BI key influencers visual to explore the FAA Wildlife Strike data to find interesting correlations. You then added the new features to your Predict Damage, Predict Size, and Predict Height ML queries. Finally, you ensured that the ML queries were cleaned up and ready for Power BI ML.

In the next chapter, you will continue to explore the FAA Wildlife Strike data using different types of capabilities in Power BI such as R and Python visuals. Newly discovered features will then be added to your ML queries as you finish them up and prepare to graduate to the Power BI cloud service.

The rest of the chapter is locked

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Authors (1)

Greg Beaumont

Greg Beaumont is a data architect at Microsoft, where he enjoys identifying and solving complex problems backed by his experience in data architecture and a passion for innovation. Focusing on the healthcare industry, Greg works closely with customers to plan enterprise analytics strategies, evaluate new tools and products, conduct training sessions and hackathons, and architect solutions that improve the quality of care and reduce costs. He strives to be a trusted advisor to his customers and is always seeking new ways to drive progress and help organizations thrive. He is a veteran of the Microsoft data speaker network and has worked with hundreds of customers on their data management and analytics strategies.

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