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所在平台: Udemy |
课程主页: https://www.udemy.com/course/microsoft-azure-ai-fundamentals-ai-900-exam-prep/
课程评论：没有评论
课程名称：微软 Azure AI 基础知识 (AI-900) 考试准备 2024 新版 课程概述： 欢迎参加在 Coursera 上的微软 Azure AI 基础知识 (AI-900) 认证课程！该课程旨在介绍学习者 Azure 云平台中人工智能 (AI) 的精彩世界。无论您是刚刚开始探索 AI，还是希望巩固对 AI 概念及 Azure AI 服务的理解，这门课程都是一个完美的起点。 课程内容： 在本课程中，您将研究各种 AI 工作负载，包括内容审核、个性化、计算机视觉、自然语言处理 (NLP)、知识挖掘、文档智能以及生成式 AI。此外，课程还将深入探讨 AI 的伦理与负责任使用，包括公平性、可靠性、安全性、隐私性、包容性、透明度和责任感等原则。该课程还将为您提供机器学习的基础知识，包括常见技术和 Azure 机器学习能力。 模块内容： 1. 描述人工智能工作负载和考虑因素 (15-20%) - 常见 AI 工作负载的概述及其特征。 - 特定 AI 应用程序的探索，如内容审核、个性化、计算机视觉、NLP、知识挖掘、文档智能和生成式 AI。 - 对负责任 AI 指导原则和设计伦理 AI 解决方案的考虑进行讨论。 2. 描述 Azure 上机器学习的基本原则 (20-25%) - 识别常见机器学习技术和场景，包括回归、分类和聚类。 - 核心机器学习概念，如特征、标签、训练和验证数据集。 - Azure 机器学习能力的概述，包括自动化机器学习、数据和计算服务以及模型管理。 3. 描述 Azure 上计算机视觉工作负载的特点 (15-20%) - 计算机视觉解决方案的常见类型，包括图像分类、物体检测、光学字符识别和面部检测。 - 支持计算机视觉的 Azure 工具和服务，如 Azure AI Vision、Azure AI 面部检测和 Azure AI 视频索引服务。 4. 描述 Azure 上自然语言处理 (NLP) 工作负载的特点 (15-20%) - 常见 NLP 工作负载的特点，包括关键词提取、实体识别、情感分析、语言模型、语音识别和翻译。 - 支持 NLP 任务的 Azure 工具和服务，包括 Azure AI 语言、Azure AI 语音和 Azure AI 翻译服务。 5. 描述 Azure 上生成式 AI 工作负载的特点 (15-20%) - 生成式 AI 解决方案和模型的介绍。 - 生成式 AI 的常见场景和负责任 AI 考虑。 - Azure OpenAI 服务的能力，包括自然语言生成、代码生成和图像生成。 课程特色： - 由经验丰富的专业人士授课，深入了解 AI 和 Azure。 - 在真实的 Azure 环境中获得实践经验。 - 测试您的知识并应用所学内容。 - 终身访问课程材料，随时随地学习。 - 本课程专门为您准备微软 Azure AI 基础知识 (AI-900) 考试，全面涵盖考试目标。 适合人群： - 对 AI 感兴趣的新人，想了解 AI 工作负载和考虑因素。 - 希望提升 AI 和机器学习技能的 IT 专业人士。 - 准备参加微软 Azure AI 基础知识 (AI-900) 认证考试的任何人。
Microsoft Azure AI Fundamentals (AI-900) Certification CourseWelcome to the Microsoft Azure AI Fundamentals (AI-900) certification course on Udemy! This engaging and comprehensive course is designed to introduce learners to the exciting world of Artificial Intelligence (AI) within the Azure cloud platform. Whether you are just starting your journey in AI or looking to solidify your understanding of AI concepts and Azure's AI services, this course is the perfect starting point.Course OverviewThroughout this course, you will explore a variety of AI workloads, including content moderation, personalization, computer vision, natural language processing (NLP), knowledge mining, document intelligence, and generative AI. You will also delve into the ethical and responsible use of AI, covering principles such as fairness, reliability, safety, privacy, inclusiveness, transparency, and accountability in AI solutions. Additionally, this course will provide you with a solid foundation in machine learning, including common techniques and Azure Machine Learning capabilities.Module BreakdownDescribe Artificial Intelligence Workloads and Considerations (15-20%)- Overview of common AI workloads and their features.- Exploration of specific AI applications like content moderation, personalization, computer vision, NLP, knowledge mining, document intelligence, and generative AI.- Discussion on the guiding principles for responsible AI and considerations for designing ethical AI solutions.Describe Fundamental Principles of Machine Learning on Azure (20-25%)- Identification of common machine learning techniques and scenarios, including regression, classification, and clustering.- Core machine learning concepts, such as features, labels, training, and validation datasets.- Overview of Azure Machine Learning capabilities, including Automated Machine Learning, data and compute services, and model management.Describe Features of Computer Vision Workloads on Azure (15-20%)- Common types of computer vision solutions, including image classification, object detection, optical character recognition, and facial detection.- Azure tools and services for computer vision, such as Azure AI Vision, Azure AI Face detection, and Azure AI Video Indexer services.Describe Features of Natural Language Processing (NLP) Workloads on Azure (15-20%)- Features of common NLP workloads, including key phrase extraction, entity recognition, sentiment analysis, language modeling, speech recognition, and translation.- Azure tools and services for NLP tasks, including Azure AI Language, Azure AI Speech, and Azure AI Translator services.Describe Features of Generative AI Workloads on Azure (15-20%)- Introduction to generative AI solutions and models.- Common scenarios for generative AI and responsible AI considerations.- Capabilities of Azure OpenAI Service, including natural language generation, code generation, and image generation.Course Features- Learn from experienced professionals with deep knowledge of AI and Azure.- Gain practical experience with AI projects in a live Azure environment.- Test your knowledge and apply what you have learned.- Access course materials anytime, anywhere, with lifetime access.- This course is specifically designed to prepare you for the Microsoft Azure AI Fundamentals (AI-900) exam, with comprehensive coverage of the exam objectives.Who Should Enroll?- Individuals new to AI who want to understand AI workloads and considerations.- IT professionals looking to enhance their skills in AI and machine learning.- Anyone preparing for the Microsoft Azure AI Fundamentals (AI-900) certification exam.
