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

Unleash the full potential of Power BI with the integration of AI and machine learning techniques using OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key Features Take flight with Power BI machine learning and OpenAI using hands-on examples from the FAA airline data Unlock the full potential of Power BI for advanced analytics using OpenAI Design stunning data presentations, seamless integration of machine learning tools and technologies with OpenAI Book Description Microsoft Power BI is the ultimate solution for businesses looking to make data-driven decisions and unlock the full potential of their data. Unleashing Your Data with Power BI Machine Learning and OpenAI is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI. The book explains the intricacies of the subject by using a workshop-style data story for data ingestion, data modeling, analytics, and predictive analytics with Power BI machine learning. Along the way, you'll learn about AI features, AI visuals, R/Python integration, and OpenAI integration. The workshop-style content allows you to practice all your learnings in real-life challenges and gain hands-on experience. Additionally, you'll gain an understanding of AI/ML, step by step, with replicable examples and references. From enhancing data visualizations to building SaaS Power BI ML models, and integrating Azure OpenAI, this book will help you unlock new capabilities in Power BI. By the end of this book, you'll be well-equipped to build ML models in Power BI, plan projects for both BI and ML, understand R/Python visuals with Power BI, and introduce OpenAI to enhance your analytics solutions. What you will learn Discover best practices for implementing AI and ML capabilities in Power BI along with integration of OpenAI into the solution Understand how to integrate OpenAI and cognitive services into Power BI Explore how to build a SaaS auto ML model within Power BI Gain an understanding of R/Python integration with Power BI Enhance data visualizations for ML feature discovery Discover how to improve existing solutions and workloads using AI and ML capabilities in Power BI with OpenAI Acquire tips and tricks for successfully using AI and ML capabilities in Power BI along with integration of OpenAI into the solution Who this book is for This book is for data science and BI professionals looking to expand their skill sets into Power BI machine learning and OpenAI. This book is also useful for data scientists, data analysts, and IT professionals who want to learn how to incorporate OpenAI into Power BI for advanced experience. Table of Contents Requirements, Data Modeling, and Planning Preparing and Ingesting Data with Power Query Exploring Data using Power BI and Creating a Semantic Model Model Data for Machine Learning in Power BI Discovering Features Using Analytics and AI Visuals Discovering New Features Using R and Python visuals Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service Building Machine Learning Models with Power BI Evaluating Trained and Tested ML Models Iterating Power BI ML Models Applying Power BI ML Models Use Cases for OpenAI Using OpenAI and Azure OpenAI in Power BI Dataflows Project Review and Looking Forward 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 Other Books You May Enjoy Unleash the full potential of Power BI with the integration of AI and machine learning techniques using OpenAI Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesTake flight with Power BI machine learning and OpenAI using hands-on examples from the FAA airline dataUnlock the full potential of Power BI for advanced analytics using OpenAIDesign stunning data presentations, seamless integration of machine learning tools and technologies with OpenAIBook DescriptionMicrosoft Power BI is the ultimate solution for businesses looking to make data-driven decisions and unlock the full potential of their data. Unleashing Your Data with Power BI Machine Learning and OpenAI is designed for data scientists and BI professionals seeking to improve their existing solutions and workloads using AI. The book explains the intricacies of the subject by using a workshop-style data story for data ingestion, data modeling, analytics, and predictive analytics with Power BI machine learning. Along the way, you'll learn about AI features, AI visuals, R/Python integration, and OpenAI integration. The workshop-style content allows you to practice all your learnings in real-life challenges and gain hands-on experience. Additionally, you'll gain an understanding of AI/ML, step by step, with replicable examples and references. From enhancing data visualizations to building SaaS Power BI ML models, and integrating Azure OpenAI, this book will help you unlock new capabilities in Power BI. By the end of this book, you'll be well-equipped to build ML models in Power BI, plan projects for both BI and ML, understand R/Python visuals with Power BI, and introduce OpenAI to enhance your analytics solutions.What you will learnDiscover best practices for implementing AI and ML capabilities in Power BI using OpenAIUnderstand how to integrate OpenAI and cognitive services into Power BIExplore how to build a SaaS auto ML model within Power BIGain an understanding of R/Python integration with Power BIEnhance data visualizations for ML feature discoveryDiscover how to improve existing solutions and workloads using AI and ML capabilities in Power BI with OpenAIAcquire tips and tricks for successfully using AI and ML capabilities in Power BI using OpenAIWho this book is forThis book is for data science and BI professionals looking to expand their skill sets into Power BI machine learning and OpenAI. This book is also useful for data scientists, data analysts, and IT professionals who want to learn how to incorporate OpenAI into Power BI for advanced experience. Unleash the full potential of Power BI with the integration of AI and Machine Learning (ML) techniques.Key Features* Embrace the future of data analysis with this comprehensive guide to AI, AutoML, and Machine Learning Connectivity in Power BI.* Discover how to unlock the full potential of Power BI with advanced AI features.* Design visually stunning data presentations, and seamless integration of cutting-edge machine learning tools and technologies.Book DescriptionMicrosoft Power BI is the ultimate solution for businesses looking to make data-driven decisions and unlock the full potential of their data. Power BI makes it easy to connect to a variety of data sources, and create stunning, interactive dashboards, reports, and visualizations that will bring your data insights to life.This book features the best practices when using Power BI AutoML, AI features, AI visuals, R/Python integration, and Azure ML integration.The book will give you a step-by-step tutorial for using different AI and ML capabilities, along with expert tips and tricks. "Workshop Style" content will also allow you to replicate the content in the book for a hands-on experience. You'll get Step-by-step AI/ML coverage with replicable examples and references. Data Scientists will learn how to leverage AI and ML capabilities in Power BI to improve their existing solutions and workloads. From enhancing data visualizations, to building a SaaS AutoML Model, to integrating Enterprise Machine Learning Solutions in Azure ML, the book will unlock new capabilities in Power BI for readers. By the end of the book, readers will have a solid understanding of how to build an AutoML model in Power BI, integrate Azure ML and Azure Cognitive Services, leverage AI visuals, and understand R/Python integration with Power BI.What you will learn* Discover best practices for implementing AI and ML capabilities in Power BI* Learn how to integrate Azure ML and Azure Cognitive Services into Power BI* Explore how to build a Sa Unleash the full potential of Power BI with the integration of AI and machine learning techniques using OpenAI ## Key Features ## Book Description By the end of this book, you'll be well-equipped to build ML models in Power BI, plan projects for both BI and ML, understand R/Python visuals with Power BI, and introduce OpenAI to enhance your analytics solutions. * Discover best practices for implementing AI and ML capabilities in Power BI along with integration of OpenAI into the solution * Understand how to integrate OpenAI and cognitive services into Power BI * Explore how to build a SaaS auto ML model within Power BI * Gain an understanding of R/Python integration with Power BI * Enhance data visualizations for ML feature discovery * Discover how to improve existing solutions and workloads using AI and ML capabilities in Power BI with OpenAI * Acquire tips and tricks for successfully using AI and ML capabilities in Power BI along with integration of OpenAI into the solution This book is for data science and BI professionals looking to expand their skill sets into Power BI machine learning and OpenAI. This book is also useful for data scientists, data analysts, and IT professionals who want to learn how to incorporate OpenAI into Power BI for advanced experience. 1. Requirements, Data Modeling, and Planning 2. Preparing and Ingesting Data with Power Query 3. Exploring Data using Power BI and Creating a Semantic Model 4. Model Data for Machine Learning in Power BI 5. Discovering Features Using Analytics and AI Visuals 6. Discovering New Features Using R and Python visuals 7. Deploying Data Ingestion and Transformation Components to the Power BI Cloud Service 8. Building Machine Learning Models with Power BI 9. Evaluating Trained and Tested ML Models 10. Iterating Power BI ML Models 11. Applying Power BI ML Models 12. Use Cases for OpenAI 13. Using OpenAI and Azure OpenAI in Power BI Dataflows 14. Project Review and Looking Forward