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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
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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

Identify features and uses for entity recognition

As we saw in the NLP scenarios section, entity recognition, also referred to as named entity recognition (NER), is one of the tasks that can be provided by NLP as part of the language area of AI.

Entity recognition identifies and classifies entities from a body of unstructured text (a corpus) that is recognized.

Entity recognition can provide the following uses:

  • Identifying people: Such as extracting names or celebrities from text such as newspaper articles/news feeds, and social media and how often they are mentioned/appear
  • Identifying countries, locations, places, and city names: Such as those mentioned in online holiday reviews, places of interest to visit, and the number of times mentioned; the most common that appear
  • Identifying brands: Such as those mentioned in social media and those that appear most often
  • Identifying companies, and organizations: Such as extracting from a contract agreement
  • Identifying...
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