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Artificial Intelligence with Power BI

You're reading from   Artificial Intelligence with Power BI Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814638
Length 348 pages
Edition 1st Edition
Languages
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Author (1):
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Mary-Jo Diepeveen Mary-Jo Diepeveen
Author Profile Icon Mary-Jo Diepeveen
Mary-Jo Diepeveen
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Table of Contents (18) Chapters Close

Preface 1. Part 1: AI Fundamentals
2. Chapter 1: Introducing AI in Power BI FREE CHAPTER 3. Chapter 2: Exploring Data in Power BI 4. Chapter 3: Data Preparation 5. Part 2: Out-of-the-Box AI Features
6. Chapter 4: Forecasting Time-Series Data 7. Chapter 5: Detecting Anomalies in Your Data Using Power BI 8. Chapter 6: Using Natural Language to Explore Data with the Q&A Visual 9. Chapter 7: Using Cognitive Services 10. Chapter 8: Integrating Natural Language Understanding with Power BI 11. Chapter 9: Integrating an Interactive Question and Answering App into Power BI 12. Chapter 10: Getting Insights from Images with Computer Vision 13. Part 3: Create Your Own Models
14. Chapter 11: Using Automated Machine Learning with Azure and Power BI 15. Chapter 12: Training a Model with Azure Machine Learning 16. Chapter 13: Responsible AI 17. Other Books You May Enjoy

Chapter 3: Data Preparation

The most important input for Artificial Intelligence (AI) is the data you have. You must ensure that you have enough data and that it is of good quality. The first step we take after importing data into Power BI is exploring the data to make sure we understand what we are working with. The next step is to clean the data and prepare it for our AI applications.

In this chapter, we'll learn about the common problems we may have with our data and how to fix them. It is important to prepare the data properly in the Power Query Editor as this will help us in extracting the right insights from the data using AI. The common problems we will talk about in this chapter are as follows:

  • Fixing the structure of your data
  • Working with missing data
  • Mitigating bias
  • Handling outliers
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