دانلود کتاب Unleashing Your Data with Power BI Machine Learning and OpenAI: Embark on a data adventure and turn your raw data into meaningful insights

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Cover
Title Page
Copyright & Credits
Contributors
Table of Contents
Preface
Part 1: Data Exploration and Preparation
Chapter 1: Requirements, Data Modeling, and Planning
Technical requirements
Reviewing the source data
Accessing the data
Exploring the FAA Wildlife Strike report data
Reviewing the requirements for the solution
Designing a preliminary data model
Flattening the data
Star schema
Hybrid design
Considerations for ML
Summary
Chapter 2: Preparing and Ingesting Data with Power Query
Technical requirements
Preparing the primary table of data
Grouping the raw data
Designing a curated table of the primary STRIKE_REPORTS data
Building a curated table of the primary STRIKE_REPORTS data
Referencing the raw table to create a new query
Keeping only the columns that you need
Data type changes
Column name changes
Building curated versions of the Aircraft Type, Engine Codes, and Engine Position queries
The Aircraft Type Info query
The Engine Position Info query
The Engine Codes Info query
Building a curated query to populate a Date table
Summary
Chapter 3: Exploring Data Using Power BI and Creating a Semantic Model
Technical requirements
Designing relationships between tables
Date table
Aircraft Type Info
Engine Codes Info
Engine Position Info
Building a Power BI dataset
Importing and processing the Wildlife Strike data queries from Power Query
Creating relationships between fact and dimension tables
Cleaning up the metadata and adjusting settings
Adding measures to your Power BI dataset
Summary
Chapter 4: Model Data for Machine Learning in Power BI
Technical requirements
Choosing features via data exploration
Adding Power Query tables to your architecture for ML training and testing
Building an analytic report to discover and choose initial features for the Predict Damage ML model
Building an analytic report to discover and choose initial features for the Predict Size ML model
Building an analytic report to discover and choose initial features for the Predict Height ML model
Creating flattened tables in Power Query for ML in Power BI
Modifying the Predict Damage table in Power Query
Modifying the Predict Size table in Power Query
Modifying the Predict Height table in Power Query
Summary
Part 2: Artificial Intelligence and Machine Learning Visuals and Publishing to the Power BI Service
Chapter 5: Discovering Features Using Analytics and AI Visuals
Technical requirements
Identifying features in Power BI using a report
Number Struck
Aircraft Mass Code
Month Num (Number)
Number of Engines
Percentage of engines struck, ingested wildlife, and were damaged
Identifying additional features using the key influencers visual in Power BI
Adding new features to the ML queries in Power Query
Summary
Chapter 6: Discovering New Features Using R and Python Visuals
Technical requirements
Exploring data with R visuals
Preparing the data for the R correlation plot
Building the R correlation plot visualization and adding it to your report
Identifying new features for your Power BI ML queries
Exploring data with Python visuals
Preparing the data for the Python histogram
Building the Python histogram visualization and add it to your report
Identifying new features for Power BI ML queries
Adding new features to the ML queries
Summary
Chapter 7: Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service
Technical requirements
Creating a Power BI workspace
Publishing your Power BI Desktop dataset and report to the Power BI cloud service
Creating Power BI dataflows with connections to source data
Dataflow 1 – reference data from the read_me.xls file
Dataflow 2 – Wildlife Strike data from the database.accdb file
Dataflow 3 – the Date table
Dataflow 4 – data to populate a Power BI dataset
Adding a dataflow for ML queries
Adding the Predict Damage ML query to a dataflow
Adding the Predict Size ML query to a dataflow
Adding the Predict Height ML query to a dataflow
Summary
Part 3: Machine Learning in Power BI
Chapter 8: Building Machine Learning Models with Power BI
Technical requirements
Building and training a binary prediction ML model in Power BI
Building and training a general classification ML model in Power BI
Building and training a regression ML model in Power BI
Summary
Chapter 9: Evaluating Trained and Tested ML Models
Technical requirements
Evaluating test results for the Predict Damage ML model in Power BI
Model performance for Predict Damage ML Model
Accuracy report for Predict Damage ML
Training Details for Predict Damage ML
Evaluating test results for Predict Size ML Model in Power BI
Model performance for Predict Size ML
Training details for Predict Size ML
Evaluating test results for the Predict Height ML model in Power BI
Model performance for Predict Height ML
Training details for Predict Height ML
Summary
Chapter 10: Iterating Power BI ML models
Technical requirements
Considerations for ML model iterations
Inaccurate data
Features with low predictive value
Data volumes
Data characteristics
Assessing the Predict Damage binary prediction ML model
Assessing the Predict Size ML classification model
Assessing the Predict Height ML regression model
Summary
Chapter 11: Applying Power BI ML Models
Technical requirements
Bringing the new FAA Wildlife strike data into Power BI
Downloading and configuring the new FAA Wildlife Strike data
Adding new FAA Wildlife Strike data to the Strike Reports dataflow
Transforming the new data to prep it for scoring with Power BI ML queries
Applying Power BI ML models to score new FAA Wildlife Strike data
Applying the Predict Damage ML model in Power BI
Applying the Predict Size ML model in Power BI
Applying the Predict Height ML model in Power BI
Summary
Part 4: Integrating OpenAI with Power BI
Chapter 12: Use Cases for OpenAI
Technical requirements
Brief overview and reference links for OpenAI and Azure OpenAI
Generating descriptions with OpenAI
Summarizing data with OpenAI
Choosing GPT models for your use cases
Summary
Chapter 13: Using OpenAI and Azure OpenAI in Power BI Dataflows
Technical requirements
Configuring OpenAI and Azure OpenAI for use in your Power BI solution
Configuring OpenAI
Configuring Microsoft Azure OpenAI
Preparing a Power BI dataflow for OpenAI and Azure OpenAI
Creating OpenAI and Azure OpenAI functions in Power BI dataflows
OpenAI and Azure OpenAI functions
Creating OpenAI and Azure OpenAI functions for Power BI dataflows
Using OpenAI and Azure OpenAI functions in Power BI dataflows
Adding a Cognitive Services function to the solution
Summary
Chapter 14: Project Review and Looking Forward
Lessons learned from the book and workshop
Exploring the intersection of BI, ML, AI, and OpenAI
ML within Power BI
Looking forward
Next steps for the FAA Wildlife Strike data solution
Next steps with Power BI and ML
Next steps for your career
Summary
Index
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