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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

Describe Core Machine Learning Concepts

In the previous chapters, you were introduced to some basic machine learning (ML) concepts, including various models and scenarios where a particular type of model might be useful. In this chapter, we’re going to explore concepts surrounding the actual data used in ML.

The objectives and skills we’ll cover in this chapter include the following:

  • Identify features and labels in a dataset for machine learning
  • Describe how training and validation datasets are used in machine learning

By the end of this chapter, you should be able to clearly articulate the terminology surrounding ML.

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