AI-900: Microsoft Azure AI Fundamentals Certification

Introduction

This 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 Exam

In 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 Considerations

This 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 Components

Students 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 Azure

This 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 Azure

Students are introduced to the fundamentals of computer vision, exploring common workloads like object detection, image classification, and face recognition. The section also covers Azure's computer vision services and custom vision capabilities for building tailored solutions.

Section 6: Natural Language Processing (NLP) on Azure

This section focuses on NLP workloads and services offered by Azure, enabling students to work with tools for text analytics, language understanding, and translation to create AI applications that process and analyze human language.

Section 7: Conversational AI on Azure

In this section, students will learn about conversational AI workloads such as chatbots and virtual assistants. Azure's conversational AI services are introduced, empowering students to design and deploy engaging conversational interfaces.

Section 8: Conclusion

The course concludes with a summary of key concepts covered, reinforcing the knowledge gained and outlining the next steps to further explore AI using Microsoft Azure services and resources.

By the end of this course, students will have a strong foundational understanding of AI and machine learning concepts, Azure AI services, and practical skills for building and managing AI solutions. This knowledge will prepare them to confidently take the AI-900 certification exam and start their journey into the world of AI.