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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practice-tests-ai-900-azure-ai-fundamental-latest/
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课程名称：实践测试 - AI-900: Azure AI 基础 课程概述： Microsoft认证：Azure AI基础 AI-900考试是一个展示对常见机器学习（ML）和人工智能（AI）工作负载理解的机会，并了解如何在Azure上实施这些工作负载。该考试适合具有技术和非技术背景的考生。虽然不要求数据科学和软件工程的经验，但一些基本的编程知识或经验将会有所帮助。Azure AI基础知识可以为其他基于角色的Azure认证如Azure数据科学家助理或Azure AI工程师助理的准备提供帮助，但并不是其先决条件。 测评技能： - 描述AI工作负载和相关考虑 - 描述Azure上的机器学习基本原理 - 描述Azure上的计算机视觉工作负载特性 - 描述Azure上的自然语言处理（NLP）工作负载特性 - 描述Azure上的对话式AI工作负载特性 随着微软在云服务市场份额的迅速增加，许多企业已开始云端之旅，因此Azure认证在就业市场上不仅受到青睐，而且非常受尊重。如果您是数据科学家、数据分析师或数据工程师，并希望进入机器学习/人工智能领域，建议您考虑通过 Microsoft Azure AI 基础 AI-900 认证来开启您的学习之旅！ 实践考试涵盖的主题： - 机器学习基本原理（不局限于Azure）（30-35%） - 人工智能工作负载（15-20%） - Azure上的计算机视觉工作负载（15-20%） - Azure上的自然语言处理（NLP）工作负载（15-20%） - Azure上的对话式AI工作负载（15-20%） AI概述： 人工智能是模拟人类行为和能力的软件创建。其关键元素包括： - 机器学习：通常是AI系统的基础，并且是我们“教育”电脑模型从数据中进行预测和得出结论的方式。 - 异常检测：自动检测系统中的错误或异常活动的能力。 - 计算机视觉：软件通过相机、视频和图像来视觉解读世界的能力。 - 自然语言处理：计算机解读书面或口头语言并作出相应反应的能力。 - 对话式AI：软件“代理”参与对话的能力。 Azure机器学习： 机器学习是大多数AI解决方案的基础。微软Azure提供以下功能： - Azure机器学习服务：一个云平台，允许您创建、管理和发布机器学习模型。 - 自动化机器学习：这一功能让非专家能够迅速从数据中创建机器学习模型。 - Azure机器学习设计器：一个允许无代码创建机器学习解决方案的界面。 - 数据和计算管理：专业数据科学家可以访问云端数据存储和计算资源以进行大规模数据实验。 - 管道：软件工程师、数据科学家和IT运营专业人员能够创建用于管理模型部署、训练和维护的管道。
Microsoft Certified: Azure AI Fundamentals AI-900This exam is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.This exam is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial.Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but it's not a prerequisite for any of them.Skills measured AI 900Describe AI workloads and considerationsDescribe fundamental principles of machine learning on AzureDescribe features of computer vision workloads on AzureDescribe features of Natural Language Processing (NLP) workloads on AzureDescribe features of conversational AI workloads on AzureMicrosoft market share in cloud services has increased exponentially in the last couple of years and many enterprises have started their journey on cloud. Hence, not only coveted, but Azure certifications are very well respected certifications in the job market too.If you are a Data Scientist, data analyst or data engineer and want to foray into the domain of Machine Learning/Artificial Intelligence, then you should consider certifying with Microsoft Azure AI Fundamentals AI 900 to begin your journey!Topics covered in these practice exam:-Fundamental principles of machine learning not limited to Azure (30-35%)-Artificial Intelligence workloads (15-20%)-Computer vision workloads on Azure (15-20%)-Natural Language Processing (NLP) workloads on Azure (15-20%)-Conversational AI workloads on Azure (15-20%)Overview of AIAI is the creation of software that imitates human behaviors and capabilities. Key elements include:Machine learning - This is often the foundation for an AI system, and is the way we "teach" a computer model to make predictions and draw conclusions from data.Anomaly detection - The capability to automatically detect errors or unusual activity in a system.Computer vision - The capability of software to interpret the world visually through cameras, video, and images.Natural language processing - The capability for a computer to interpret written or spoken language, and respond in kind.Conversational AI - The capability of a software "agent" to participate in a conversation.Azure Machine LearningMachine Learning is the basis of most AI solutions.Microsoft Azure offers the following: Azure Machine Learning Service - A cloud-based platform that allows you to create, manage and publish machine learning models. Azure Machine Learning offers the following capabilities and features:Automated machine-learning: This feature allows non-experts to create machine learning models quickly from data.Azure Machine Learning designer: An interface that allows for no-code creation of machine learning solutions.Data and compute management: Professional data scientists can access cloud-based data storage and compute resources to run code for data experiments at scale.Pipelines: Software engineers, data scientists, and IT operations professionals are able to create pipelines that can be used to manage model deployment, training, and management.
