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Artificial Intelligence with Power BI

Artificial Intelligence with Power BI

Mary-Jo Diepeveen

Kup pozycję na ebookpoint.pl

The artificial intelligence (AI) capabilities in Power BI enable organizations to quickly and easily gain more intelligent insights from unstructured and structured data.

This book will teach you how to make use of the many AI features available today in Power BI to quickly and easily enrich your data and gain better insights into patterns that can be found in your data.

You'll begin by understanding the benefits of AI and how it can be used in Power BI. Next, you'll focus on exploring and preparing your data for building AI projects and then progress to using prominent AI features already available in Power BI, such as forecasting, anomaly detection, and Q&A. Later chapters will show you how to apply text analytics and computer vision within Power BI reports. This will help you create your own Q&A functionality in Power BI, which allows you to ask FAQs from another knowledge base and then integrate it with PowerApps. Toward the concluding chapters, you'll be able to create and deploy AutoML models trained in Azure ML and consume them in Power Query Editor. After your models have been trained, you'll work through principles such as privacy, fairness, and transparency to use AI responsibly.

By the end of this book, you'll have learned when and how to enrich your data with AI using the out-of-the-box AI capabilities in Power BI.

  • Artificial Intelligence with Power BI
  • Contributors
  • About the author
  • About the reviewers
  • Preface
    • Who this book is for
    • What this book covers
    • To get the most out of this book
    • Download the example code files
    • Download the color images
    • Conventions used
    • Get in touch
    • Share Your Thoughts
  • Part 1: AI Fundamentals
  • Chapter 1: Introducing AI in Power BI
    • What do we expect from a data analyst?
      • What is a data analyst?
      • Connecting to data
      • Visualizing data
    • What is AI?
      • Understanding the definition of AI
      • Understanding machine learning
      • Understanding deep learning
      • Understanding supervised and unsupervised learning
      • Understanding algorithms
      • What is the data science process?
    • Why should we use AI in Power BI?
      • The problems with implementing AI
      • Why AI in Power BI is the solution
    • What are our options for AI in Power BI?
      • Out-of-the-box options
      • Creating your own models
    • Summary
  • Chapter 2: Exploring Data in Power BI
    • Technical requirements
      • Using the sample dataset on world happiness
      • How to interpret this dataset
      • Importing the world happiness dataset into Power BI
    • What to look for in your data
      • Understanding data quantity
      • Understanding data quality
    • Using data profiling tools
      • Column quality
      • Column distribution
      • Column profile
    • Using visuals to explore your data
      • Line charts
      • Bar charts
      • Histograms
      • Scatter plots
      • matplotlib
    • Summary
  • Chapter 3: Data Preparation
    • Fixing the structure of your data
      • Working with structured data
      • Fixing the structure of semi-structured data
      • Fixing the structure when working with images
    • Working with missing data
      • How do you find missing data?
      • What do you do with missing data?
    • Mitigating bias
      • How to find bias
      • How to mitigate bias in your dataset
    • Handling outliers
    • Summary
  • Part 2: Out-of-the-Box AI Features
  • Chapter 4: Forecasting Time-Series Data
    • Technical requirements
    • Data requirements for forecasting
      • Why use forecasting?
      • Time-series data
      • Using an example tourism data
    • Algorithms used for forecasting
      • The benefit of using an out-of-the-box feature
      • Understanding how forecasting is calculated in Power BI
      • Optimizing forecasting accuracy in Power BI
    • Using forecasting in Power BI
    • Summary
    • Further reading
  • Chapter 5: Detecting Anomalies in Your Data Using Power BI
    • Technical requirements
    • Which data is suitable for anomaly detection?
      • Why use anomaly detection?
      • Data requirements for anomaly detection
    • Understanding the logic behind anomaly detection
      • The algorithms behind Microsofts anomaly detection feature
      • No need to label your data
      • Fast and powerful analysis
    • Using anomaly detection in Power BI
      • Importing the sample dataset into Power BI
      • Enabling anomaly detection in Power BI
    • Summary
    • Further reading
  • Chapter 6: Using Natural Language to Explore Data with the Q&A Visual
    • Technical requirements
    • Understanding natural language processing
      • Using natural language in programs
      • Understanding natural language for data exploration
      • Preparing data for natural language models
    • Creating a Q&A visual in Power BI
      • Adding a Q&A visual
      • Using the Q&A visual
    • Optimizing your Q&A visual
      • Exploring the Q&A setup
      • Improving the Q&A experience
      • Using feedback to improve the model over time
    • Summary
    • Further reading
  • Chapter 7: Using Cognitive Services
    • Technical requirements
    • Understanding Azure's Cognitive Services
      • Creating a Cognitive Services resource
    • Using Cognitive Services for LU
      • Using Azure's Text Analytics
      • Creating question answering from a knowledge base
    • Using Cognitive Services for CV
      • Understanding Azure's Computer Vision
      • Using Azure's Custom Vision
      • Using the Face service
    • Summary
  • Chapter 8: Integrating Natural Language Understanding with Power BI
    • Technical requirements
    • Using Language APIs in Power BI Desktop
      • Using AI Insights
      • Using Power Query Editor
    • Visualizing insights from text in reports
      • Visualizing text with a Word Cloud
    • Summary
  • Chapter 9: Integrating an Interactive Question and Answering App into Power BI
    • Technical requirements
    • Creating a question answering service
      • Understanding the application of question answering
      • Configuring a question answering service
    • Creating an FAQ app with Power Apps
      • Creating a new app with Power Apps
      • Adding Power Automate to call the question answering service
      • Connecting Power Automate to Power Apps
    • Integrating the FAQ app with Power BI
    • Improving the question answering model
    • Summary
  • Chapter 10: Getting Insights from Images with Computer Vision
    • Technical requirements
    • Getting insights with Computer Vision using AI Insights
      • Using the Vision option of AI Insights
    • Configuring Custom Vision
      • Preparing the data for Custom Vision
      • Training the model in Custom Vision
      • Evaluating classification models
      • Publishing your Custom Vision model
    • Integrating Computer Vision or Custom Vision with Power BI
    • Using visuals to show a reel of images in a report
      • Storing data and ensuring it is anonymously accessible
      • Improving the Custom Vision model
    • Summary
  • Part 3: Create Your Own Models
  • Chapter 11: Using Automated Machine Learning with Azure and Power BI
    • Technical requirements
    • Understanding AutoML
      • Understanding the ML process
      • Improving the performance of an ML model
      • When to use AutoML
    • Creating an AutoML experiment in Azure ML
      • Creating an Azure ML workspace and resources
      • Configuring an AutoML run
    • Deploying a model to an endpoint
    • Integrating the model with Power BI
    • Summary
  • Chapter 12: Training a Model with Azure Machine Learning
    • Technical requirements
    • Understanding how to train a model
      • Understanding the machine learning process
    • Working with Azure ML
      • Creating Azure ML assets
    • Training a model with Azure ML Designer
      • Configuring an Azure ML Designer pipeline
    • Deploying a model for batch or real-time predictions
      • Generating batch predictions
      • Generating real-time predictions
    • Integrating an endpoint with Power BI to generate predictions
    • Summary
  • Chapter 13: Responsible AI
    • Understanding responsible AI
    • Protecting privacy when using personal data
      • Removing personally identifiable information
      • Using differential privacy on personal data
    • Creating transparent models
      • Using algorithms that are transparent by design
      • Explaining black-box models
    • Creating fair models
      • Identifying unfairness in models
      • Mitigating unfairness in models
    • Summary
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