Microsoft Azure AI Fundamentals

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Microsoft Azure AI Fundamentals (AI-900) Course

Duration

2 Days Instructor-led Course

Delivery Option

Instructor-led Classroom-based Training

or

Instructor-led Digital-based Live Training

Target Audience for AI-900

  • Business stakeholders, new and existing IT professionals, consultants, and students who want to gain foundational knowledge of AI workloads and Azure services.
  • Anyone interested in learning how AI solutions can be built using Microsoft Azure, regardless of prior Azure experience.

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Microsoft Azure AI Fundamentals Course Overview

The Microsoft Azure AI Fundamentals (AI-900) course introduces participants to fundamental concepts related to AI and the services in Microsoft Azure that can be used to create AI solutions. While the course does not focus on developing participants into professional data scientists or software developers, it builds awareness of common AI workloads and provides the ability to identify appropriate Azure services to support them. The goal of this course is to provide participants with all the tools needed to prepare for the AI-900 Microsoft Azure AI Fundamentals exam, covering key concepts such as AI workloads, responsible AI principles, machine learning, computer vision, natural language processing, and conversational AI on Azure.

Microsoft Azure Administrator Course Prerequisites

No specific certification prerequisites are required. However, successful participants typically have:

  • Experience using computers and the Internet.
  • An interest in AI applications and machine learning models.
  • A basic understanding of computer technology and the ability to interpret charts.

Azure Administrator Course Objectives

Upon successful completion of this course, participants will be able to:

  • Describe AI workloads and considerations.
  • Explain fundamental principles of machine learning on Azure.
  • Describe features of computer vision workloads on Azure.
  • Understand features of Natural Language Processing (NLP) workloads on Azure.
  • Identify features of conversational AI workloads on Azure.

Microsoft Azure AI Fundamentals Course Components

  • Training: Live sessions led by our teaching team of experienced professional trainers.
  • Lessons: Comprehensive modules to prepare for core AI-900 topics.
  • Video Learning: Demonstrations covering fundamental cloud concepts and Azure services.
  • Practice Test: Includes Practice and Certification modes for exam readiness.
  • Lab Suite: Hands-on labs designed to build familiarity with Azure services and the Azure portal.

Course Outline

Lesson 1: Describe Artificial Intelligence Workloads and Considerations
  • Skill 1.1: Identify features of common AI workloads
    • Overview of AI workloads
    • Understanding Azure Cognitive Services, Azure Machine Learning, and Azure Bot Service
  • Skill 1.2: Identify guiding principles for Responsible AI
    • Fairness, Reliability & Safety, Privacy & Security, Inclusiveness, Transparency, and Accountability
    • Responsible AI for Bots and Microsoft’s AI for Good program
Lesson 2: Describe the Fundamental Principles of Machine Learning on Azure
  • Skill 2.1: Identify common machine learning types
    • Regression, classification, and clustering models
  • Skill 2.2: Describe core machine learning concepts
    • Workflow, training and validation datasets, and model evaluation
  • Skill 2.3: Identify core tasks in creating a machine-learning solution
    • Data ingestion, feature selection, model training, and deployment
  • Skill 2.4: Describe capabilities of no-code machine learning with Azure Machine Learning
    • Azure Automated ML and Azure ML Designer
Lesson 3: Describe Features of Computer Vision Workloads on Azure
  • Skill 3.1: Identify common types of computer vision solutions
    • Image classification, object detection, and optical character recognition
  • Skill 3.2: Identify Azure tools and services for computer vision tasks
    • Computer Vision, Custom Vision, Face, and Form Recogniser services
Lesson 4: Configure and Manage Virtual Networking
  • Skill 4.1: Identify features of common NLP workload scenarios
    • Language modeling, key phrase extraction, and sentiment analysis
  • Skill 4.2: Identify Azure tools and services for NLP workloads
    • Text Analytics, Language Understanding (LUIS), Speech, and Translator services
Lesson 5: Describe Features of Conversational Workloads on Azure
  • ● Skill 5.1: Identify common use cases for conversational AI
    • Webchat bots and conversational AI solutions
  • Skill 5.2: Identify Azure services for conversational AI
    • QnA Maker and Azure Bot Service

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