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Power BI Machine Learning and OpenAI

You're reading from   Power BI Machine Learning and OpenAI Explore data through business intelligence, predictive analytics, and text generation

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Product type Paperback
Published in May 2023
Publisher Packt
ISBN-13 9781837636150
Length 308 pages
Edition 1st Edition
Languages
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Author (1):
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Greg Beaumont Greg Beaumont
Author Profile Icon Greg Beaumont
Greg Beaumont
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Table of Contents (21) Chapters Close

Preface 1. Part 1: Data Exploration and Preparation
2. Chapter 1: Requirements, Data Modeling, and Planning FREE CHAPTER 3. Chapter 2: Preparing and Ingesting Data with Power Query 4. Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model 5. Chapter 4: Model Data for Machine Learning in Power BI 6. Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
7. Chapter 5: Discovering Features Using Analytics and AI Visuals 8. Chapter 6: Discovering New Features Using R and Python Visuals 9. Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service 10. Part 3: Machine Learning in Power BI
11. Chapter 8: Building Machine Learning Models with Power BI 12. Chapter 9: Evaluating Trained and Tested ML Models 13. Chapter 10: Iterating Power BI ML models 14. Chapter 11: Applying Power BI ML Models 15. Part 4: Integrating OpenAI with Power BI
16. Chapter 12: Use Cases for OpenAI 17. Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows 18. Chapter 14: Project Review and Looking Forward 19. Index 20. Other Books You May Enjoy

Adding a dataflow for ML queries

Now that you’ve ingested, cleaned up, and transformed the data from the FAA Wildlife Strike database, you can build out your specialized queries for Power BI ML models. Before you get started, note that Power BI ML is a version of Azure AutoML that has been built into Power BI as a SaaS offering. Data science teams using advanced tools will often apply transformations to data, such as imputing missing values, normalizing numeric ranges, and weighting features within a model. The advanced transformations of features won’t be covered in this book since AutoML has featurization capabilities to optimize data for ML. The queries you will be creating could probably be improved upon with advanced featurization techniques, but for this project, we will keep things simple and let the AutoML featurization capabilities in Power BI ML handle some of the advanced feature transformations.

Adding the Predict Damage ML query to a dataflow

You will...

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