Integrating artificial intelligence into business analytics is not just a trend but a strategic advantage. The manual "Artificial Intelligence with Microsoft Power BI" reveals how to leverage machine learning and AI within the Power BI platform - from visual forecasting to integrating Azure AI and cognitive services.
The authors offer a structured, practice-oriented approach that’s accessible not only to technical experts but also to analysts without deep programming knowledge. You’ll gain a clear understanding of how to connect, process, and visualize data using Power BI’s built-in tools and Microsoft’s extensions.
Where to Download "Artificial Intelligence with Microsoft Power BI"?
The manual is available for download in PDF on codersguild.net, a professional platform for developers, analysts, and data engineers. Here, you’ll find high-quality technical textbooks, handpicked and vetted by experts. CodersGuild.net offers PDF versions without registration, ads, or malicious files. Each edition is well-structured, making it ideal for self-study or corporate training. The platform regularly updates its library with topics like Power BI, Azure, Python, DevOps, AI architecture, and more.
Who Should Read This Textbook?
The authors target professionals who need to integrate AI into BI processes without diving deeply into code.
- Analysts working with Power BI: The textbook enhances standard dashboards with intelligent analytics.
- Data scientists and ML engineers: It explains how to integrate machine learning models with visual analytics.
- Business analysts and data managers: It covers AI use cases for improving decision-making quality.
- BI solution developers in Azure: It provides practices for connecting cognitive services and ML models via Azure.
What Sets "Artificial Intelligence with Microsoft Power BI" Apart?
This isn’t another guide on building charts. It’s a practical manual for integrating AI tools into daily analytics. Unlike many textbooks limited to basic visualization, this one emphasizes intelligent data processing - from predictive analytics to automated text and image analysis.
A key distinction is its integration with the Microsoft Azure ecosystem. The authors detail how to connect Power BI with cognitive services, Custom Vision, Text Analytics, Azure ML, and AutoML. This approach enables not just data analysis but enrichment through neural networks and AI algorithms.
The textbook is built on real-world cases: sales forecasting, customer segmentation, and sentiment analysis. Each example explains not only the tools but also the logic behind data-driven decisions.
Additionally, the authors show how to ensure transparency and interpretability of AI models in Power BI visualizations - a critical aspect for business clients. The approach is grounded in practical technology application and aligns with modern analytics and data security requirements.
How to Apply the Knowledge in Practice?
The textbook’s content is directly applicable to real BI projects:
- Automating sales and demand forecasts based on historical data
- Processing customer feedback and sentiment analysis with Text Analytics
- Integrating Power BI with Azure ML and AutoML
- Creating interactive AI dashboards with explainable insights
- Building KPI monitoring systems with intelligent alerts
Each example can be adapted to business needs, from retail to finance.
More About the Author of the Book
The Developer's Opinion About the Book
This is one of the few textbooks that doesn’t limit Power BI to a visualization tool but reveals it as a platform for integrating AI into business analytics. The authors demonstrate practical ways to connect artificial intelligence - from Azure ML to Text Analytics - while keeping the material accessible. The coverage of both technical and ethical/business aspects of AI integration is particularly valuable. This textbook is useful for analysts, data engineers, and solution architects aiming to go beyond standard reports and build next-generation analytical systems. I recommend it to anyone using Power BI in commercial projects who wants to enhance analytics with AI.
Sarah Bennett, Machine Learning Developer
FAQ for "Artificial Intelligence with Microsoft Power BI"
1. Do I need Azure knowledge to understand the material?
No, the authors explain Azure integration with Power BI from scratch. For most scenarios, a basic Azure account and interface familiarity are enough. The textbook guides you step-by-step on creating cognitive service resources, connecting APIs, and interpreting results in reports. No DevOps, ML, or infrastructure knowledge is required. If you’re new to Azure, you’ll gain a clear understanding of its capabilities and how to connect AI tools.
2. Can this textbook be used in commercial projects?
Yes. All approaches are scalable and applicable in real-world corporate analytics. It covers scenarios for processing large datasets, forecasting, text recognition, image processing, and building explainable AI models. The textbook addresses both technical and business aspects: model interpretability, GDPR compliance, and preparing reports for leadership. This makes it suitable for production BI projects in retail, logistics, finance, and healthcare. The examples include guidance on adapting them to your own data.
3. Are there sections on natural language processing (NLP)?
Yes, the textbook covers integration with the Text Analytics API for sentiment analysis, entity extraction, and key phrase identification. It shows how to analyze customer reviews, emails, and forms in Power BI without coding. This is especially useful for CRM systems, support teams, and marketing. It also explores text translation, language detection, and combining NLP outputs with visualizations. The author provides tips on selecting the right model and analyzing results.
4. Is the textbook suitable for Power BI beginners?
Partially. Basic skills in Power BI Desktop and visualizations are recommended. However, it doesn’t require deep technical knowledge, as integrations are explained step-by-step. If you can build dashboards and work with tables, you’ll easily grasp the material. The textbook is a logical next step after mastering a basic Power BI course, showing how to move from static charts to intelligent analytics while keeping the interface simple. This blend of simplicity and depth makes it an excellent guide for analysts leveling up.
5. Are there examples of visualizing predictive models?
Yes, it covers scenarios like sales forecasting, seasonality, and customer behavior using AutoML and Azure ML. You’ll learn to display probabilistic forecasts, confidence intervals, and anomalies visually. Power BI enables both model visualization and explanation, which is critical for business users. The textbook covers visual storytelling techniques: presenting AI predictions clearly, building intuitive scorecards, and adding explanations to charts.
6. Can third-party ML models be integrated?
Yes. The textbook covers working with models deployed in Azure ML and third-party APIs. It explains how to connect REST interfaces, send data, and retrieve predictions. Examples include scenarios with custom Python models and TensorFlow via cloud services. Special attention is given to forming JSON requests and transforming results for Power BI. This allows Power BI to serve as a powerful frontend for analyzing predictions from any external model.
Information
| Author: | Jennifer Stirrup and Thomas J. Weinandy | Language: | English |
| Publisher: | O'Reilly Media | ISBN-13: | 978-1098112752 |
| Publication Date: | May 7, 2024 | ISBN-10: | 109811275X |
| Print Length: | 470 pages | Category: | Machine Learning and Artificial Intelligence Books |
Get PDF version of "Artificial Intelligence with Microsoft Power BI" by Jennifer Stirrup and Thomas J. Weinandy
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