Overview

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Build a practical foundation in artificial intelligence and learn how AI workloads are implemented with Microsoft Azure services while preparing for the AI-900 Microsoft Azure AI Fundamentals certification. You’ll explore essential AI and machine learning concepts, common AI workloads, dataset structures, model types, and evaluation metrics such as AUC and FPR. Designed for beginners, business users, and technical decision-makers, this course requires no programming expertise. You’ll learn to identify suitable AI use cases, apply Microsoft’s responsible AI principles, and understand fairness, accountability, transparency, and security in AI solutions. Through hands-on examples and service walkthroughs, you’ll configure an Azure Machine Learning workspace, use AutoML, and construct no-code machine learning pipelines with Azure ML Designer. You’ll also examine computer vision, natural language processing, and conversational AI using Computer Vision, Text Analytics, Azure Bot Services, and QnA Maker. By the end of the course, you’ll be able to differentiate machine learning approaches, interpret model performance, build and deploy no-code models as web services, analyze text, identify computer vision services, and design conversational bots. Its combination of AI-900 exam alignment, responsible AI guidance, and visual, no-code Azure workflows makes this course a focused path to understanding and applying Azure AI.

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.