From the course: Microsoft Azure AI Fundamentals (AI-900) Cert Prep by Microsoft Press

Introduction - Azure AI Services Tutorial

From the course: Microsoft Azure AI Fundamentals (AI-900) Cert Prep by Microsoft Press

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Introduction

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- Hello, I'm Tim Warner. Welcome to this comprehensive video training course for Exam AI-900: Microsoft Azure AI Fundamentals, 2nd Edition. With over 26 years in information technology, I'm a Microsoft certified trainer and have served for six years as a Microsoft most valuable professional focused on Azure artificial intelligence and cloud. I'm excited to guide you through the fascinating world of artificial intelligence with Microsoft Azure. This course carefully covers every exam objective, offering practical insights and test-taking strategies. I encourage you to dive deeper using the course materials available in our public GitHub course repository, go.techtrainertim.com/ai900. go.techtrainertim.com/ai900. Now let's preview what each lesson covers. Lesson one: identify features of common AI workloads. We cover the characteristics of AI applications, content moderation, personalization, computer vision, natural language processing, knowledge mining, document intelligence, and generative AI workloads. Lesson two: identify guiding principles for responsible AI. We examine AI ethics, discussing fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability. Lesson three: identify common machine learning techniques. We explore foundational scenarios, such as regression, classification, clustering and deep learning to build your understanding of AI's predictive power. Lesson four: describe Azure machine learning capabilities. We focus on Azure machine learning features, including creating and managing data sets, automating machine learning, and deploying models within Azure. Lesson five: identify common types of computer vision solutions. We dive into image classification, object detection, optical character recognition, facial detection, and Azure's AI vision and face services. Lesson six: identify features of common natural language processing workload scenarios. We explore capabilities from key phrase extraction and entity recognition to sentiment analysis, language modeling, speech recognition, translation, and Azure's AI language and speech services. Lesson seven: identify features of generative AI solutions. We investigate generative AI models, common use cases, and responsible AI considerations for generative technologies. Lesson eight: identify capabilities of Azure OpenAI service. We conclude with Azure OpenAI service, highlighting natural language generation, code generation, and image generation capabilities at the cutting edge of today's AI landscape. Are you ready to become certified in Microsoft Azure AI? Excellent! Let's get started.

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