AI-900: Microsoft Azure AI Fundamentals

AI-900: Microsoft Azure AI Fundamentals

Offered by Linux Training

The AI-900: Microsoft Azure AI Fundamentals course at Linux Training is designed for beginners, students, and professionals who want to understand the basics of Artificial Intelligence (AI) and how it is implemented using Microsoft Azure.

This course provides a strong foundation in AI concepts, machine learning, computer vision, and natural language processing, along with an introduction to Azure AI services.

Course Overview

This program introduces core concepts of Artificial Intelligence and Azure AI services, helping learners understand how AI solutions are built and used in real-world applications. It is ideal for those starting their journey in AI and cloud technologies.

What You Will Learn

  • Fundamentals of Artificial Intelligence
  • Machine Learning Basics
  • Computer Vision Concepts
  • Natural Language Processing (NLP)
  • Generative AI Overview
  • Azure AI Services Introduction
  • Responsible AI Principles

Why Choose This Course?

  • Beginner-friendly certification
  • No coding experience required
  • Industry-recognized certification (AI-900)
  • Strong foundation for advanced AI courses
  • Hands-on exposure to Azure AI tools

Career Opportunities

After completing this course, you can explore roles such as:

  • AI Fundamentals Associate
  • Cloud AI Associate
  • Data & AI Support Executive
  • Business Analyst (AI-focused)
  • IT Support Associate

Who Can Join?

  • Students and fresh graduates
  • Non-IT and IT beginners
  • Professionals exploring AI careers
  • Anyone interested in Artificial Intelligence

Start Your AI Journey with Azure

Join Linux Training and build a strong foundation in AI concepts to step into the future of intelligent technologies.

AI-900: Microsoft Azure AI Fundamentals

Modules

1. Describe Artificial Intelligence workloads and considerations (15–20%)

Identify features of common AI workloads

  • Identify computer vision workloads
  • Identify natural language processing workloads
  • Identify document processing workloads
  • Identify features of generative AI workloads

    • Identify guiding principles for responsible AI

  • Describe considerations for fairness in an AI solution
  • Describe considerations for reliability and safety in an AI solution
  • Describe considerations for privacy and security in an AI solution
  • Describe considerations for inclusiveness in an AI solution
  • Describe considerations for transparency in an AI solution
  • Describe considerations for accountability in an AI solution

    • 2. Describe fundamental principles of machine learning on Azure (15–20%)

    Identify common machine learning techniques

  • Identify regression machine learning scenarios
  • Identify classification machine learning scenarios
  • Identify clustering machine learning scenarios
  • Identify features of deep learning techniques
  • Identify features of the Transformer architecture

    • Describe core machine learning concepts

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

    • Describe Azure Machine Learning capabilities

  • Describe capabilities of automated machine learning
  • Describe data and compute services for data science and machine learning
  • Describe model management and deployment capabilities in Azure Machine Learning

    • 3. Describe features of computer vision workloads on Azure (15–20%)

    Identify common types of computer vision solution

  • Identify features of image classification solutions
  • Identify features of object detection solutions
  • Identify features of optical character recognition solutions
  • Identify features of facial detection and facial analysis solutions

    • Identify Azure tools and services for computer vision tasks

  • Describe capabilities of the Azure AI Vision service
  • Describe capabilities of the Azure AI Face detection service

    • 4. Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%)

    Identify features of common NLP workload scenarios

  • Identify features and uses for key phrase extraction
  • Identify features and uses for entity recognition
  • Identify features and uses for sentiment analysis
  • Identify features and uses for language modeling
  • Identify features and uses for speech recognition and synthesis
  • Identify features and uses for translation

    • Identify Azure tools and services for NLP workloads

  • Describe capabilities of the Azure AI Language service
  • Describe capabilities of the Azure AI Speech service

    • 5. Describe features of generative AI workloads on Azure (20–25%)

    Identify features of generative AI solutions

  • Identify features of generative AI models
  • Identify common scenarios for generative AI
  • Identify responsible AI considerations for generative AI

    • Identify generative AI services and capabilities in Microsoft Azure

  • Describe features and capabilities of Azure AI Foundry
  • Describe features and capabilities of Azure OpenAI service
  • Describe features and capabilities of Azure AI Foundry model catalog

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