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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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Toc

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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

absolute error 39

accuracy 46

AdaBoost 32

adaptation 93

AI knowledge mining 11

features 11, 12

AI Personalizer 8

AI translation 171

algorithm 30

Anomaly Detector 5

Application Insights 89, 90

application programming interfaces (APIs) 5

artificial intelligence (AI) 3, 72, 158

Artificial Neural Networks (ANNs) 32

Assistants playground 218

configuring 218-220

attached compute 85

attention mechanism 194, 195

attention score 195

attributes 136

automated machine learning (AutoML) 78

capabilities 79, 80

ensemble models 82

feature engineering 82

test scenarios 82

training 82

use cases 80

used, for training model 96-104

validation 82

AutoML use cases

classification 80

computer vision 81

natural language...

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