When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is currently available only to learners who have paid or received financial aid, when available.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AI-900 - Azure AI Fundamentals and Two Practice Tests

AI-900 - Azure AI Fundamentals and Two Practice Tests

位教师：Packt - Course Instructors

包含在 Coursera Plus 中

了解更多

7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

9 小时完成

灵活的计划

自行安排学习进度

7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

9 小时完成

灵活的计划

自行安排学习进度

您将学到什么

Gain a clear understanding of AI concepts and their application in real-world scenarios using Azure services
Learn the differences between AI, Machine Learning, and Data Science, and how they interrelate
Develop a foundational knowledge of Azure Machine Learning, Cognitive Services, and other key AI tools on Azure
Prepare for the AI-900 exam with real-world example questions and effective strategies for answering exam questions

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

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7个模块

This course features Coursera Coach!

A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this course, you will gain a strong understanding of Azure AI fundamentals, preparing you for the Microsoft AI-900 exam. From grasping the core concepts of Artificial Intelligence (AI) to understanding how machine learning, computer vision, and natural language processing work on Azure, you’ll learn how AI is applied in real-world scenarios. By the end of this course, you will be able to describe Azure AI services, workloads, and principles in depth, providing you with the skills to develop AI solutions using Azure's advanced tools. Throughout the course, you’ll explore fundamental AI concepts and tools such as Azure Cognitive Services, Azure Machine Learning, and Azure Cognitive Search. You'll dive into machine learning workflows, covering regression, classification, and clustering techniques, and understand how Azure facilitates these processes. The course also offers insights into the practical application of AI through demonstrations and hands-on sessions with Azure tools. Whether you're new to Azure or aiming to solidify your knowledge, this course will guide you step-by-step through the fundamentals of Azure AI. The curriculum also prepares you for the AI-900 exam with real-world example questions, helping you hone your critical thinking and exam techniques. This course is ideal for individuals seeking to break into AI with Microsoft Azure, those preparing for the AI-900 certification exam, or anyone interested in understanding AI workloads on the Azure platform. No prior AI or Azure knowledge is required, making it suitable for beginners in the field.

In this module, we will provide an overview of the AI-900 exam, walking through its contents and key topics. We’ll also discuss strategies for effective exam preparation, ensuring you’re ready for success on test day.

涵盖的内容

1个视频1篇阅读材料

In this module, we will dive into the core concepts of Artificial Intelligence, differentiating it from other fields like Machine Learning and Data Science. We will also explore Azure’s AI services, responsible AI principles, and how these technologies can be used effectively within Microsoft Azure.

涵盖的内容

9个视频1个作业1个插件

9个视频总计23分钟

What is Artificial Intelligence?2分钟
Differences between Artificial Intelligence, Machine Learning and Data Science6分钟
Common Artificial Intelligence Workloads (Machine Learning, Anomaly Detection)1分钟
Artificial Intelligence in Microsoft Azure1分钟
Challenges and Risks with Artificial Intelligence4分钟
Six Principles of Responsible Artificial Intelligence1分钟
Azure Machine Learning1分钟
Azure Machine Learning and Azure Cognitive Services3分钟
Azure Cognitive Search1分钟

1个作业总计15分钟

Objective 1: Describe AI Workloads and Considerations - Assessment15分钟

1个插件总计15分钟

Understanding AI, Machine Learning, and Data Science15分钟

In this module, we will explore fundamental machine learning principles, focusing on techniques like regression, classification, and clustering. We’ll also cover the tools and features within Azure Machine Learning that help streamline the machine learning process.

涵盖的内容

6个视频1个作业1个插件

6个视频总计9分钟

What is Machine Learning?1分钟
Regression2分钟
Classification1分钟
Clustering1分钟
A Deeper Dive into Azure Machine Learning2分钟
Automated Machine Learning and Azure Machine Learning Designer1分钟

1个作业总计15分钟

Objective 2: Describe Fundamental Principles of Machine Learning on Azure - Assessment15分钟

1个插件总计15分钟

Fundamentals of Machine Learning Techniques15分钟

In this module, we will explore how Azure’s computer vision services can be applied to solve real-world challenges. From analyzing images and videos to training custom models, you will gain insight into the diverse applications of computer vision in Azure.

涵盖的内容

10个视频1个作业1个插件

10个视频总计23分钟

What is Computer Vision?1分钟
Applications of Computer Vision2分钟
A Demo of Applying Computer Vision Services6分钟
More Azure Cognitive Services1分钟
The Computer Vision Service and Image Analysis with Computer Vision Service3分钟
Training Models with the Custom Vision Service and Analyzing Faces1分钟
Reading Text with the Computer Vision Service and Video Indexer. Video Analysis1分钟
Custom Insights1分钟
Video Indexer Widgets and API1分钟
Analyzing Forms with the Form Recognizer Service1分钟

1个作业总计15分钟

Objective 3: Describe features of computer vision workloads on Azure - Assessment15分钟

1个插件总计15分钟

Introduction to Azure Computer Vision15分钟

In this module, we will delve into the core features of Natural Language Processing (NLP) on Azure, covering text and speech analytics, sentiment analysis, translation, and language understanding tools. You’ll learn how to build intelligent systems that process and respond to human language.

涵盖的内容

12个视频1个作业1个插件

12个视频总计28分钟

What is Natural Language Processing (NLP)?1分钟
Natural Language Processing in Azure0分钟
The Text Analytics Service2分钟
Text Analytics, Speech Recognition and Synthesis1分钟
The Translator Service1分钟
Detection, Translation, Transliteration and Translation3分钟
Introduction to Language Understanding1分钟
Language Understanding (LUIS) Resources in Azure2分钟
Intents and Utterances3分钟
Entities5分钟
Patterns, Patterns.Any() Entities and Language Understanding1分钟
Demo Speech Services - Speech to Text and Translation3分钟

1个作业总计15分钟

Objective 4: Describe Features of Natural Language Processing Workloads on Azure - Assessment15分钟

1个插件总计15分钟

Understanding NLP Workloads on Azure15分钟

In this module, we will focus on building conversational AI solutions using Azure services such as QnA Maker and Language Understanding. We will guide you through creating intelligent bots, enhancing them with active learning, and deploying them in real-world scenarios.

涵盖的内容

6个视频1个作业1个插件

6个视频总计10分钟

What is Conversational AI?2分钟
AI Guidelines for Responsible Bots1分钟
Conversational AI in Azure: The QnA Maker Service2分钟
QnA Maker vs Language Understanding1分钟
Active Learning1分钟
Creating a QnA Bot1分钟

1个作业总计15分钟

Objective 5: Describe features of conversational AI Workloads on Azure - Assessment15分钟

1个插件总计15分钟

Features of Conversational AI on Azure15分钟

In this module, we will prepare you for the AI-900 exam by reviewing example questions and offering strategies for effectively answering them. You will learn how to approach the questions with a critical mindset and improve your overall exam readiness.

涵盖的内容

7个视频3个作业

7个视频总计194分钟

Be Prepared to Answer Questions for AI-900: Example Questions (Part 1)5分钟
How to Think when Answering Questions for AI-900: Example Questions15分钟
How to Answer Questions for AI-900: Example Questions19分钟
Be Prepared to Answer Questions for AI-900: Example Questions (Part 1)26分钟
Be Prepared to Answer Questions for AI-900: Example Questions (Part 2)42分钟
Be Prepared to Answer Questions for AI-900: Example Questions (Part 3)48分钟
Be Prepared to Answer Questions for AI-900: Example Questions (Part 4)37分钟

3个作业总计90分钟

Full Course Practice Assessment15分钟
Answering Questions for AI-900: How to think and how to answer - Assessment15分钟
Full Course Assessment60分钟

位教师

Packt - Course Instructors

Packt

971 门课程232,003 名学生

提供方

Packt

从 Machine Learning 浏览更多内容

状态：免费试用
Microsoft
Preparing for AI-900: Microsoft Azure AI Fundamentals exam
课程
状态：免费试用
Microsoft
Microsoft Azure AI Fundamentals AI-900 Exam Prep
专项课程
状态：预览
Whizlabs
Exam Prep AI-102: Microsoft Azure AI Engineer Associate
课程
状态：免费试用
Microsoft
Intro to Artificial Intelligence on Microsoft Azure
课程

人们为什么选择 Coursera 来帮助自己实现职业发展

Felipe M.

自 2018开始学习的学生

''能够按照自己的速度和节奏学习课程是一次很棒的经历。只要符合自己的时间表和心情，我就可以学习。'

Jennifer J.

自 2020开始学习的学生

''我直接将从课程中学到的概念和技能应用到一个令人兴奋的新工作项目中。'

Larry W.

自 2021开始学习的学生

''如果我的大学不提供我需要的主题课程，Coursera 便是最好的去处之一。'

Chaitanya A.

''学习不仅仅是在工作中做的更好：它远不止于此。Coursera 让我无限制地学习。'

通过 Coursera Plus 开启新生涯

无限制访问 10,000+ 世界一流的课程、实践项目和就业就绪证书课程 - 所有这些都包含在您的订阅中

了解更多

通过在线学位推动您的职业生涯

获取世界一流大学的学位 - 100% 在线

探索学位

加入超过 3400 家选择 Coursera for Business 的全球公司

提升员工的技能，使其在数字经济中脱颖而出

了解更多

常见问题

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.