AI-900: Microsoft Azure AI Fundamentals
Overview
The AI-900 certification introduces learners to the core concepts of Artificial Intelligence (AI)
and how they are implemented using Microsoft Azure services. This foundational course is
designed for individuals with both technical and non-technical backgrounds who want to
explore how AI can solve business challenges using machine learning, computer vision, natural
language processing, and more.
AI-900 is perfect for business professionals, project managers, students, and aspiring AI practitioners looking to understand the power and possibilities of AI—without needing prior coding or data science experience.
AI-900 is perfect for business professionals, project managers, students, and aspiring AI practitioners looking to understand the power and possibilities of AI—without needing prior coding or data science experience.
What You’ll Learn
By the end of this course, you will be able to:
- Understand fundamental AI concepts such as machine learning, neural networks, and
deep learning - Explore common AI workloads and how they’re used in business scenarios
- Identify Microsoft Azure services for building AI solutions (e.g., Azure Cognitive Services,
Azure Machine Learning) - Recognize capabilities of natural language processing, speech, and vision-based AI
- Understand responsible AI principles: fairness, transparency, accountability, and privacy
- Describe how to provision, test, and manage simple AI workloads on Azure
Prerequisites
- No prior coding or AI knowledge required
- Suitable for business decision-makers, students, and early-career tech professionals
- Familiarity with cloud basics (e.g., AZ-900) is helpful but optional
Course Content Outline
1. Describe AI Workloads and Considerations
- Defining AI and its types
- Common AI workloads: prediction, classification, clustering, etc.
- Key principles of responsible AI (ethics, fairness, bias, privacy)
2. Describe Fundamental Principles of Machine Learning on Azure
- Types of machine learning: supervised, unsupervised, reinforcement
- Components of a machine learning pipeline
- Introduction to Azure Machine Learning workspace and AutoML
3. Describe Features of Computer Vision Workloads on Azure
- Image classification, object detection, and facial recognition
- Azure Computer Vision API and Custom Vision
- Use cases in retail, manufacturing, and public safety
4. Describe Features of Natural Language Processing (NLP) Workloads on Azure
- Language understanding, text analytics, and translation
- Azure services: Language Understanding (LUIS), Text Analytics API
- AI use in sentiment analysis and document summarization
5. Describe Features of Conversational AI Workloads on Azure
- Building chatbots using Azure Bot Service
- Power Virtual Agents for low-code bot creation
- Integrating bots with Teams and websites