|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/developing-ai-applications-azure
课程评论：没有评论
课程名称：在Azure上开发人工智能应用 概述：本课程介绍了人工智能和机器学习的基本概念。我们将讨论机器学习的类型和任务，以及机器学习算法。您将探索Python这一受欢迎的机器学习编程语言，并使用一些科学生态系统包来帮助实现机器学习解决方案。接下来，本课程将介绍Microsoft Azure中可用的机器学习工具。我们将回顾数据分析的标准化方法，并为您提供具体的指导，介绍Microsoft的团队数据科学方法。在课程中，您将接触到Microsoft的预训练和管理的机器学习模型，这些模型通过REST API在其认知服务套件中提供。我们将实现计算机视觉API和面部识别API的解决方案，并通过调用自然语言服务进行情感分析。 使用Azure机器学习服务，您将创建并使用Azure机器学习工作区。接着，您将训练自己的模型，并在云端部署和测试该模型。在整个课程中，您将进行实践练习，以掌握新的人工智能技能。完成本课程后，您将能够创建、实现和部署机器学习模型。 课程大纲： 1. 人工智能简介：介绍人工智能和机器学习，讨论机器学习的类型和任务，机器学习算法，探索Python及其科学生态系统包。学习结束后，您将能够在至少一个Python机器学习库中实现机器学习模型。 2. 标准化AI流程和Azure资源：介绍Microsoft Azure的机器学习工具，探讨成功的数据分析项目的标准化方法，提供Microsoft团队数据科学方法的具体指导。课程结束时，您将学习如何在Microsoft的DevOps解决方案中实施此过程。 3. Azure认知API：介绍Microsoft的预训练和管理的机器学习REST API，具体实现计算机视觉API、面部识别API，并通过调用自然语言服务进行情感分析。 4. Azure机器学习服务：模型训练：介绍Azure机器学习服务的功能，探讨如何创建和引用ML工作区，训练机器学习模型，并介绍实验、运行和模型的作用及目的。通过练习创建工作区、构建计算目标和执行训练运行。 5. Azure机器学习服务：模型管理和部署：讨论如何连接到工作区，模型注册的工作原理，以及如何从本地和工作区训练运行注册训练模型。演示为模型部署做准备的步骤，包括识别依赖关系、配置部署目标、构建容器镜像，最终将训练模型作为Web服务进行部署并通过发送JSON对象测试API。
Name:Introduction to Artificial Intelligence
Description:This module introduces Artificial Intelligence and Machine learning. Next, we talk about machine learning types and tasks. This leads into a discussion of machine learning algorithms. Finally we explore python as a popular language for machine learning solutions and share some scientific ecosystem packages which will help you implement machine learning. By the end of this unit you will be able to implement machine learning models in at least one of the available python machine learning libraries.
Name:Standardized AI Processes and Azure Resources
Description:This module introduces machine learning tools available in Microsoft Azure. It then looks at standardized approaches developed to help data analytics projects to be successful. Finally, it gives you specific guidance on Microsoft's Team Data Science Approach to include roles and tasks involved with the process. The exercise at the end of this unit points you to Microsoft's documentation to implement this process in their DevOps solution if you don't have your own.
Name:Azure Cognitive APIs
Description:This module introduces you to Microsoft's pretrained and managed machine learning offered as REST API's in their suite of cognitive services. We specifically implement solutions using the computer vision api, the facial recognition api, and do sentiment analysis by calling the natural language service.
Name:Azure Machine Learning Service: Model Training
Description:This module introduces you to the capabilities of the Azure Machine Learning Service. We explore how to create and then reference an ML workspace. We then talk about how to train a machine learning model using the Azure ML service. We talk about the purpose and role of experiments, runs, and models. Finally, we talk about Azure resources available to train your machine learning models with. Exercises in this unit include creating a workspace, building a compute target, and executing a training run using the Azure ML service.
Name:Azure Machine Learning Service: Model Management and Deployment
Description:This module covers how to connect to your workspace. Next, we discuss how the model registry works and how to register a trained model locally and from a workspace training run. In addition, we show you the steps to prepare a model for deployment including identifying dependencies, configuring a deployment target, building a container image. Finally, we deploy a trained model as a webservice and test it by sending JSON objects to the API.
This course introduces the concepts of Artificial Intelligence and Machine learning. We'll discuss machine learning types and tasks, and machine learning algorithms. You'll explore Python as a popular programming language for machine learning solutions, including using some scientific ecosystem packages which will help you implement machine learning. Next, this course introduces the machine learning tools available in Microsoft Azure. We'll review standardized approaches to data analytics and you'll receive specific guidance on Microsoft's Team Data Science Approach. As you go through the course, we'll introduce you to Microsoft's pre-trained and managed machine learning offered as REST API's in their suite of cognitive services. We'll implement solutions using the computer vision API and the facial recognition API, and we'll do sentiment analysis by calling the natural language service. Using the Azure Machine Learning Service you'll create and use an Azure Machine Learning Worksace.Then you'll train your own model, and you'll deploy and test your model in the cloud. Throughout the course you will perform hands-on exercises to practice your new AI skills. By the end of this course, you will be able to create, implement and deploy machine learning models.
