Exam AI-900 Microsoft Azure AI Fundamentals

Video description

3+ Hours of Video Instruction

Prepare to become an AI professional and ace the Microsoft Exam AI-900 with our comprehensive video course from Microsoft Press, covering everything from the fundamentals of AI to practical applications using Azure AI services.

The technologies covered in Microsoft Exam AI-900, including machine learning, natural language processing, computer vision, and cognitive services, are widely used in real-world applications across various industries.

In healthcare, AI is used for medical image analysis, drug discovery, and predicting patient outcomes. In finance, AI is used for fraud detection, credit scoring, and algorithmic trading. In retail, AI is used for recommendation systems, supply chain optimization, and inventory management. In manufacturing, AI is used for predictive maintenance, quality control, and autonomous robotics. In customer service, AI is used for chatbots, sentiment analysis, and personalized marketing.

These are just a few examples of how AI technologies are being used in the real world, and the demand for professionals with the skills to develop and implement these technologies is rapidly increasing.

About the Instructor

As an experienced and knowledgeable instructor with a background in AI and Microsoft Azure, Tim Warner is optimally suited to provide expert guidance and insights on the subject matter. The author presents the material in an engaging and easy-to-understand way that will resonate with the audience. Tim has 25 years of experience as a Microsoft technical professional, and Microsoft Certified Trainer (MCT). He is a Five-year veteran of the Microsoft MVP program. He has widespread knowledge of Microsoft AI technologies as a Microsoft FTE attached to several Azure product engineering teams, and has extensive experience teaching on Microsoft AI products, services, and certifications.

Skill Level:

  • Beginner
  • Intermediate

What You Will Learn:

After completing this video, you will be able to:

  • Comprehend Artificial Intelligence workloads and considerations
  • Understand fundamental principles of machine learning on Azure
  • Work with all features of computer vision workloads on Azure
  • Experiment with features of Natural Language Processing (NLP) workloads on Azure

Who Should Take This Course:

  • Data analysts or scientists who want to expand their skills to include AI and machine learning.
  • Software developers or engineers who want to incorporate AI capabilities into their software applications.
  • IT professionals who want to learn how to leverage AI technologies in their organization.
  • Project managers or team leaders who want to understand the potential impact of AI on their organization.
  • Students or recent graduates who want to gain a basic understanding of AI concepts and how they can be applied in different industries.

Prerequisite:

This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, awareness of cloud basics and client-server applications would be beneficial.

More about Microsoft Press:

Microsoft Press creates IT books and references for all skill levels across the range of Microsoft technologies.

https://www.microsoftpressstore.com/

About Pearson Video Training:

Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que. Topics include IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.

Table of contents

  1. Introduction
    1. Exam AI-900 Microsoft Azure AI Fundamentals: Introduction
  2. Lesson 1: Identify Features of Common AI Workloads
    1. Learning objectives
    2. 1.1 Identify features of anomaly detection workloads
    3. 1.2 Identify computer vision workloads
    4. 1.3 Identify natural language processing workloads
    5. 1.4 Identify knowledge mining workloads
  3. Lesson 2: Identify Guiding Principles for Responsible AI
    1. Learning objectives
    2. 2.1 Describe considerations for fairness in an AI solution
    3. 2.2 Describe considerations for reliability and safety in an AI solution
    4. 2.3 Describe considerations for privacy and security in an AI solution
    5. 2.4 Describe considerations for inclusiveness in an AI solution
    6. 2.5 Describe considerations for transparency in an AI solution
    7. 2.6 Describe considerations for accountability in an AI solution
  4. Lesson 3: Identify Common Machine Learning Types
    1. Learning objectives
    2. 3.1 Identify regression machine learning scenarios
    3. 3.2 Identify classification machine learning scenarios
    4. 3.3 Identify clustering machine learning scenarios
  5. Lesson 4: Describe Core Machine Learning Concepts
    1. Learning objectives
    2. 4.1 Identify features and labels in a dataset for machine learning
    3. 4.2 Describe how training and validation datasets are used in machine learning
  6. Lesson 5: Describe Capabilities of Visual Tools in Azure Machine Learning Studio
    1. Learning objectives
    2. 5.1 Automated machine learning
    3. 5.2 Azure Machine Learning designer
  7. Lesson 6: Identify Common Types of Computer Vision Solution
    1. Learning objectives
    2. 6.1 Identify features of image classification solutions
    3. 6.2 Identify features of object detection solutions
    4. 6.3 Identify features of optical character recognition solutions
    5. 6.4 Identify features of facial detection and facial analysis solutions
  8. Lesson 7: Identify Azure Tools and Services for Computer Vision Tasks
    1. Learning objectives
    2. 7.1 Identify capabilities of the Computer Vision service
    3. 7.2 Identify capabilities of the Custom Vision service
    4. 7.3 Identify capabilities of the Face service
    5. 7.4 Identify capabilities of the Form Recognizer service
  9. Lesson 8: Identify Features of Common NLP Workload Scenarios
    1. Learning objectives
    2. 8.1 Identify features and uses for key phrase extraction
    3. 8.2 Identify features and uses for entity recognition
    4. 8.3 Identify features and uses for sentiment analysis
    5. 8.4 Identify features and uses for language modeling
    6. 8.5 Identify features and uses for speech recognition and synthesis
    7. 8.6 Identify features and uses for translation
  10. Lesson 9: Identify Azure Tools and Services for NLP Workloads
    1. Learning objectives
    2. 9.1 Identify capabilities of the Language service
    3. 9.2 Identify capabilities of the Speech service
    4. 9.3 Identify capabilities of the Translator service
  11. Lesson 10: Identify Considerations for Conversational AI Solutions on Azure
    1. Learning objectives
    2. 10.1 Identify features and uses for bots
    3. 10.2 Identify capabilities of the Power Virtual Agents and Azure Bot service
  12. Summary
    1. Exam AI-900 Microsoft Azure AI Fundamentals: Summary

Product information

  • Title: Exam AI-900 Microsoft Azure AI Fundamentals
  • Author(s): Tim Warner
  • Release date: July 2023
  • Publisher(s): Pearson
  • ISBN: 0138202788