AI-900: Microsoft Azure AI Fundamentals Certification

IntroductionThis course is designed for individuals aspiring to gain foundational knowledge of artificial intelligence (AI) and machine learning (ML) concepts while preparing for the AI-900: Microsoft Azure AI Fundamentals Certification Exam. Through hands-on demonstrations and practical examples, students will learn about AI workloads, responsible AI principles, and Azure AI services to build, deploy, and manage AI models effectively.Section 1: Introduction to AI and the AI-900 ExamIn this section, students are introduced to the basics of artificial intelligence and machine learning, the significance of the AI-900 certification, and the essential requirements for the exam. Topics include an overview of AI concepts, the structure of machine learning models, and a clear breakdown of exam prerequisites.Section 2: AI Workloads and Responsible AI ConsiderationsThis section explores common AI workloads and Microsoft's principles for responsible AI. Students will learn about key considerations like privacy, security, transparency, and accountability when working with AI systems. Official Microsoft AI resources are also introduced to reinforce best practices.Section 3: Machine Learning Types and Core ComponentsStudents will dive deeper into machine learning concepts, including types of ML (supervised, unsupervised, and reinforcement learning), dataset features and labels, training and validation datasets, and evaluation metrics like FPR (false positive rate) and AUC (area under the curve).Section 4: No-Code Machine Learning with AzureThis section highlights the power of no-code ML tools like AutoML and Azure ML Designer. Through practical demos, students will learn to create an Azure ML workspace, build no-code models using AutoML, and design workflows with ML Designer for streamlined AI development.Section 5: Computer Vision Workloads on A