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Microsoft Azure AI Fundamentals AI-900 Exam Guide

You're reading from   Microsoft Azure AI Fundamentals AI-900 Exam Guide Gain proficiency in Azure AI and machine learning concepts and services to excel in the AI-900 exam

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Product type Paperback
Published in May 2024
Publisher Packt
ISBN-13 9781835885666
Length 288 pages
Edition 1st Edition
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Authors (2):
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Steve Miles Steve Miles
Author Profile Icon Steve Miles
Steve Miles
Aaron Guilmette Aaron Guilmette
Author Profile Icon Aaron Guilmette
Aaron Guilmette
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Table of Contents (20) Chapters Close

Preface 1. Part 1: Identify Features of Common AI Workloads FREE CHAPTER
2. Chapter 1: Identify Features of Common AI Workloads 3. Chapter 2: Identify the Guiding Principles for Responsible AI 4. Part 2: Describe the Fundamental Principles of Machine Learning on Azure
5. Chapter 3: Identify Common Machine Learning Techniques 6. Chapter 4: Describe Core Machine Learning Concepts 7. Chapter 5: Describe Azure Machine Learning Capabilities 8. Part 3: Describe Features of Computer Vision Workloads on Azure
9. Chapter 6: Identify Common Types of Computer Vision Solutions 10. Chapter 7: Identify Azure Tools and Services for Computer Vision Tasks 11. Part 4: Describe Features of Natural Language Processing (NLP) Workloads on Azure
12. Chapter 8: Identify Features of Common NLP Workload Scenarios 13. Chapter 9: Identify Azure Tools and Services for NLP Workloads 14. Part 5: Describe Features of Generative AI Workloads on Azure
15. Chapter 10: Identify Features of Generative AI Solutions 16. Chapter 11: Identify Capabilities of Azure OpenAI Service 17. Chapter 12: Accessing the Online Practice Resources 18. Index 19. Other Books You May Enjoy

What this book covers

Chapter 1, Identify Features of Common AI Workloads, introduces some of the basic concepts of AI in the Azure platform space.

Chapter 2, Identify the Guiding Principles for Responsible AI, explains Microsoft’s principles for responsible AI, such as transparency and inclusiveness.

Chapter 3, Identify Common Machine Learning Techniques, explores machine learning techniques, such as clustering and regression.

Chapter 4, Describe Core Machine Learning Concepts, expands on the concepts of machine learning techniques with explanations of features, labels, training, and validation.

Chapter 5, Describe Azure Machine Learning Capabilities, focuses on the power of automated machine learning (AutoML) as well as the functional resources necessary for enabling machine learning in Azure.

Chapter 6, Identify Common Types of Computer Vision Solutions, introduces the concepts behind computer vision, such as optical character recognition and object detection.

Chapter 7, Identify Azure Tools and Services for Computer Vision Tasks, expands on the basics of computer vision with services such as Azure AI Vision and Azure AI Face.

Chapter 8, Identify Features of Common NLP Workload Scenarios, introduces the core foundational workload uses for natural language processing, such as sentiment analysis, translation, and key phrase extraction.

Chapter 9, Identify Azure Tools and Services for NLP Workloads, provides information about Azure’s natural language processing solutions, such as the Azure AI Translator and Azure AI Language services.

Chapter 10, Identify Features of Generative AI Solutions, explains the broad features and use cases for generative AI models.

Chapter 11, Identify Capabilities of Azure OpenAI Service, highlights the powerful features of Azure OpenAI Service, including text content and image generation.

Chapter 12, Accessing the Online Practice Resources.

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