Microsoft Azure AI Fundamentals AI-900 Exam Guide

Microsoft Azure AI Fundamentals AI-900 Exam Guide

位教师：Packt - Course Instructors

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11个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

11个模块

深入了解一个主题并学习基础知识。

初级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

您将学到什么

Discover and apply various AI workloads and services in Azure.
Understand Microsoft's principles for responsible AI development.
Explore the fundamentals of AI and machine learning in practical scenarios.

您将获得的技能

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

11 项作业

授课语言：英语（English）

了解顶级公司的员工如何掌握热门技能

了解关于 Coursera for Business 的更多信息

Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

该课程共有11个模块

This course provides a clear understanding of core AI concepts, Microsoft Azure AI services, and the essential strategies behind responsible and effective AI implementation. Learners will gain foundational knowledge that is increasingly valuable across technology, business, and data-driven roles.

By exploring real Azure tools and services, you will learn how to apply AI workloads, understand machine learning fundamentals, and recognize key use cases in computer vision, natural language processing, and generative AI. This course equips you with the practical insights needed to confidently prepare for the AI-900 certification and apply your skills professionally. The curriculum blends theory with hands-on examples, providing both conceptual clarity and demonstrations of Azure Machine Learning, Azure Cognitive Services, and Azure OpenAI Service. You’ll see how real-world AI solutions are designed, deployed, and evaluated. This course is ideal for beginners, aspiring AI practitioners, IT professionals, and business users looking to understand AI on Azure. No prior coding or data science experience is required.

In this section, we explore key features of Azure AI workloads, including data monitoring, content moderation, computer vision, and NLP, emphasizing their practical applications in business solutions.

涵盖的内容

2个视频4篇阅读材料1个作业

2个视频总计2分钟

Course Overview 1分钟
Identify Features of Common AI Workloads - Overview Video 1分钟

4篇阅读材料总计35分钟

Introduction 5分钟
Identify Features of Data Monitoring and Anomaly Detection Workloads 10分钟
Identify Computer Vision Workloads 10分钟
Identify Document Intelligence Workloads 10分钟

1个作业总计10分钟

Exploring AI Workload Characteristics 10分钟

In this section, we cover key considerations for responsible AI development, including accountability, inclusiveness, and reliability.

涵盖的内容

1个视频2篇阅读材料1个作业

In this section, we explore regression, classification, and clustering machine learning techniques, focusing on their practical applications and evaluation metrics for data analysis and prediction.

涵盖的内容

1个视频7篇阅读材料1个作业

1个视频总计1分钟

Identify Common Machine Learning Techniques - Overview Video 1分钟

7篇阅读材料总计100分钟

Introduction 10分钟
Unsupervised Machine Learning Techniques 20分钟
Mean Squared Error 20分钟
Specificity 10分钟
Evaluation Metrics 20分钟
Davies-Bouldin Index 10分钟
Identify Features of Deep Learning Techniques 10分钟

1个作业总计10分钟

Foundations of Machine Learning Techniques 10分钟

In this section, we explore features, labels, and data preparation in machine learning, focusing on training and validation datasets and their roles in model development.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore Azure ML capabilities, including AutoML, data services, and model deployment.

涵盖的内容

1个视频5篇阅读材料1个作业

In this section, we cover computer vision solutions, including image, object, and facial analysis features.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore Azure AI Vision, Face, and Video Indexer services for image analysis, facial recognition, and video insights, emphasizing their pre-built and customizable machine learning capabilities.

涵盖的内容

1个视频2篇阅读材料1个作业

In this section, we explore NLP workload features like key phrase extraction, entity recognition, and sentiment analysis with practical applications.

涵盖的内容

1个视频3篇阅读材料1个作业

In this section, we explore Azure AI services for NLP tasks, focusing on language analysis, speech processing, and translation capabilities to support application development with text, speech, and multilingual content.