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所在平台: Udemy |
课程主页: https://www.udemy.com/course/ai-900-microsoft-azure-ai-fundamentals-certification-m/
课程评论：没有评论
课程名称：AI-900：微软Azure人工智能基础认证 概述：本课程旨在为有志于获取人工智能（AI）和机器学习（ML）基础知识的人士设计，同时为AI-900：微软Azure人工智能基础认证考试做好准备。通过实践演示和实际示例，学生将学习AI工作负载、负责任的AI原则以及Azure AI服务，以有效构建、部署和管理AI模型。 第一部分：AI与AI-900考试简介 本部分介绍了人工智能和机器学习的基础知识，AI-900认证的重要性以及考试的基本要求。主题包括AI概念概述、机器学习模型结构以及考试前提条件的详细说明。 第二部分：AI工作负载与负责任的AI考量 本部分探讨了常见的AI工作负载及微软的负责任AI原则。学生将学习在处理AI系统时的关键考量因素，如隐私、安全、透明度和问责制。还介绍了微软的AI官方资源，以强化最佳实践。 第三部分：机器学习类型与核心组件 学生将深入了解机器学习概念，包括监督学习、非监督学习和强化学习类型、数据集特征与标签、训练和验证数据集，以及评估指标，如假阳性率（FPR）和曲线下面积（AUC）。 第四部分：使用Azure进行无代码机器学习 本部分突出无代码ML工具的强大功能，如AutoML和Azure ML Designer。通过实际演示，学生将学习如何创建Azure ML工作区、使用AutoML构建无代码模型，以及使用ML Designer设计简化AI开发工作流。 第五部分：Azure上的计算机视觉工作负载 学生将学习计算机视觉的基础知识，探索常见的工作负载，如目标检测、图像分类和人脸识别。本节还涵盖了Azure的计算机视觉服务和自定义视觉能力，以构建量身定制的解决方案。 第六部分：Azure上的自然语言处理（NLP） 本部分重点关注NLP工作负载和Azure提供的服务，使学生能够使用文本分析、语言理解和翻译工具创建处理和分析人类语言的AI应用。 第七部分：Azure上的对话式AI 在本部分，学生将学习对话式AI工作负载，如聊天机器人和虚拟助手。介绍Azure的对话式AI服务，帮助学生设计和部署引人入胜的对话界面。 第八部分：总结 课程结束时将总结关键概念，强化所学知识，并概述进一步探索AI的下一步，利用微软Azure服务和资源。 完成本课程后，学生将具备扎实的AI和机器学习基础知识，了解Azure AI服务，并掌握构建和管理AI解决方案的实践技能。这些知识将使他们有信心参加AI-900认证考试，并开启AI的探索之旅。
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 AzureStudents 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 AzureThis 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 AzureIn 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: ConclusionThe 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.
