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所在平台: Udemy |
课程主页: https://www.udemy.com/course/microsoft-ai-900/
课程评论：没有评论
课程名称：AI-900：Microsoft Azure人工智能基础 课程概述： 本课程基于最新大纲，旨在为学员提供人工智能的基本知识。即使计划日后参加相关考试，参加该课程也能帮助您清晰理解AI基础。如果您希望开始探索Azure，这门课程亦是您的理想选择，可以帮助您在人工智能的云端旅程上起步。课程无需编写代码，重点在于理解基本概念。 课程内容涵盖以下技能： 1. **描述人工智能工作负载和考虑因素（15-20%）** - 识别常见AI工作负载的特征，包括预测/预测工作负载、异常检测工作负载、计算机视觉工作负载、自然语言处理或知识挖掘工作负载、会话AI工作负载。 - 确定负责任AI的指导原则，包括公平性、可靠性与安全性、隐私与安全性、包容性、透明性和问责制的考虑因素。 2. **描述Azure机器学习的基本原则（30-35%）** - 识别常见的机器学习类型，如回归、分类和聚类场景。 - 描述核心机器学习概念，包括特征、标签、训练与验证数据集、算法模型训练和模型评估指标的选择与解释。 - 识别创建机器学习解决方案中的核心任务，如数据引入和准备、特征选择与工程、模型训练与评估及模型部署和管理的共同特征。 - 描述Azure机器学习中的无代码机器学习功能，如自动机器学习工具和Azure机器学习设计器。 3. **描述Azure上的计算机视觉工作负载特征（15-20%）** - 识别常见的计算机视觉解决方案类型，包括图像分类、物体检测、语义分割、光学字符识别、面部检测、识别及分析解决方案。 - 识别Azure在计算机视觉任务中可用的工具和服务。 4. **描述Azure上的自然语言处理（NLP）工作负载特征（15-20%）** - 识别常见NLP工作负载场景的特征及用途，如关键词提取、实体识别、情感分析、语言建模、语音识别与合成及翻译。 - 识别Azure在NLP工作负载中可用的工具和服务。 5. **描述Azure上的会话AI工作负载特征（15-20%）** - 识别会话AI的常见应用场景，如网页聊天机器人、电话语音菜单和个人数字助手。 - 识别Azure在会话AI中可用的服务。 通过参加本课程，您将打下坚实的人工智能基础，为进入Azure云服务的世界做好准备。
This is the course based on latest syllabus , by attending this course you will be gaining the fundamental knowledge on Artificial Intelligence. Even if you are planning to write the exam later then also you can go through this course it will help you to understand and clear your basic for AI.If you are looking to start your journey into the Azure then this course is for you too. You can start your journey into the cloud with Artificial Intelligence. There is no need to write any code. You need to understand the basics.You will be taught below Skills MeasuredDescribe Artificial Intelligence workloads and considerations (15-20%)Identify features of common AI workloads· identify prediction/forecasting workloads· identify features of anomaly detection workloads· identify computer vision workloads· identify natural language processing or knowledge mining workloads· identify conversational AI workloads Identify guiding principles for responsible AI· describe considerations for fairness in an AI solution· describe considerations for reliability and safety in an AI solution· describe considerations for privacy and security in an AI solution· describe considerations for inclusiveness in an AI solution· describe considerations for transparency in an AI solution· describe considerations for accountability in an AI solutionDescribe fundamental principles of machine learning on Azure (30- 35%)Identify common machine learning types · identify regression machine learning scenarios· identify classification machine learning scenarios· identify clustering machine learning scenarios Describe core machine learning concepts· identify features and labels in a dataset for machine learning· describe how training and validation datasets are used in machine learning· describe how machine learning algorithms are used for model training· select and interpret model evaluation metrics for classification and regression Identify core tasks in creating a machine learning solution· describe common features of data ingestion and preparation· describe common features of feature selection and engineering· describe common features of model training and evaluation· describe common features of model deployment and management Describe capabilities of no-code machine learning with Azure Machine Learning:· automated Machine Learning tool· azure Machine Learning designerDescribe features of computer vision workloads on Azure (15-20%)Identify common types of computer vision solution:· identify features of image classification solutions· identify features of object detection solutions· identify features of semantic segmentation solutions· identify features of optical character recognition solutions· identify features of facial detection, recognition, and analysis solutions Identify Azure tools and services for computer vision tasks· identify capabilities of the Computer Vision service· identify capabilities of the Custom Vision service· identify capabilities of the Face service· identify capabilities of the Form Recognizer service Describe features of Natural Language Processing (NLP) workloads on Azure (15-20%) Identify features of common NLP Workload Scenarios· identify features and uses for key phrase extraction· identify features and uses for entity recognition· identify features and uses for sentiment analysis· identify features and uses for language modeling· identify features and uses for speech recognition and synthesis· identify features and uses for translation Identify Azure tools and services for NLP workloads· identify capabilities of the Text Analytics service· identify capabilities of the Language Understanding Intelligence Service (LUIS)· identify capabilities of the Speech service· identify capabilities of the Text Translator serviceDescribe features of conversational AI workloads on Azure (15-20%)Identify common use cases for conversational AI· identify features and uses for webchat bots· identify features and uses for telephone voice menus· identify features and uses for personal digital assistants Identify Azure services for conversational AI· identify capabilities of the QnA Maker service· identify capabilities of the Bot Framework
