AI-900 MS Azure AI Fundamentals

Overview

This course provides a comprehensive introduction to artificial intelligence (AI) and its implementation using Microsoft Azure services, aligned with the AI-900 certification. Learners will explore core AI concepts including machine learning, responsible AI practices, and data processing fundamentals. Through hands-on examples and service walkthroughs, the course demystifies common AI workloads such as computer vision, natural language processing (NLP), and conversational AI. Participants will learn to analyze AI use cases, classify different types of machine learning models, and apply Azure-based tools like AutoML, ML Designer, Text Analytics, and Azure Bot Services to create no-code or low-code AI solutions. Emphasis is also placed on ethical considerations and responsible deployment of AI technologies. Designed for beginners, business users, and technical decision-makers, this course enables learners to understand, evaluate, and implement AI-driven applications using the Microsoft Azure ecosystem—empowering them to contribute meaningfully to intelligent solution development in modern organizations.

Syllabus

Foundations of AI and Microsoft’s Ethical Principles

This module introduces learners to the fundamental concepts of Artificial Intelligence (AI) and its real-world applications. It explains what AI is, how machine learning models function, and the types of workloads typically handled by AI systems. Additionally, the module outlines the structure and requirements of the AI-900 certification exam and explores Microsoft’s approach to responsible AI. Learners will gain insights into AI’s ethical principles, including fairness, accountability, transparency, and security, setting the stage for informed and responsible use of AI technologies within the Microsoft Azure ecosystem.

Machine Learning Fundamentals and No-Code Tools

This module delves into the foundational concepts and practical workflows of machine learning within the Azure ecosystem. Learners explore common machine learning types, understand the structure of datasets, and evaluate models using key metrics such as AUC and FPR. The module further introduces Azure’s no-code tools such as AutoML and ML Designer, guiding learners through the complete lifecycle of model creation—from data registration to deployment—without requiring programming expertise. Through visual and automated experiences, learners will develop a solid grasp of how to build, train, and deploy models efficiently in Azure Machine Learning.

Cognitive Services and Conversational AI

This module explores how Microsoft Azure supports a wide range of AI services focused on perception and interaction. Learners will gain foundational knowledge of computer vision, natural language processing (NLP), and conversational AI workloads. They will examine practical applications, explore relevant Azure services, and understand how to build solutions using tools like Computer Vision, Text Analytics, and Azure Bot Services—enabling intelligent interactions through visual recognition, text comprehension, and conversational interfaces.