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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/microsoft-azure-machine-learning-for-data-scientist
课程评论：没有评论
课程名称：Microsoft Azure 机器学习与数据科学家 概述：机器学习是人工智能的核心，许多现代应用和服务依赖于预测性机器学习模型。训练机器学习模型是一个迭代的过程，需要时间和计算资源，而自动化机器学习可以简化这一过程。在本课程中，您将学习如何使用 Azure 机器学习创建和发布模型，而无需编写代码。 本课程是一个五门课程项目中的第二门课程，旨在帮助您参加 DP-100：在 Azure 上设计和实施数据科学解决方案的认证考试。通过认证考试，您将有机会证明自己在使用 Azure 机器学习以云规模操作机器学习解决方案的知识和专业技能。这个专项课程教授您如何利用现有的 Python 和机器学习知识，管理数据摄取和准备、模型培训和部署，以及机器学习解决方案的监控。 该专项课程适合已经掌握 Python 和机器学习框架（如 Scikit-Learn、PyTorch 和 Tensorflow）的数据科学家，旨在帮助他们构建和运营云中的机器学习解决方案。学生将学习如何在 Microsoft Azure 中创建端到端解决方案，包括管理机器学习的 Azure 资源、运行实验和训练模型、部署和运营机器学习解决方案，以及实施负责任的机器学习。同时，他们还将学习如何使用 Azure Databricks 探索、准备和建模数据，并将 Databricks 机器学习过程与 Azure 机器学习集成。 课程大纲： 1. 使用 Azure 机器学习中的自动化机器学习 - 描述：训练机器学习模型是一个迭代过程，需要时间和计算资源。自动化机器学习可以更简化。在本模块中，您将学习如何识别不同类型的机器学习模型，以及如何利用 Azure 机器学习的自动化机器学习功能训练和部署预测模型。 2. 使用 Azure 机器学习设计器创建回归模型 - 描述：回归是一种监督式机器学习技术，用于预测数值。在本模块中，您将学习如何使用 Azure 机器学习设计器创建回归模型。 3. 使用 Azure AI 创建分类模型 - 描述：分类是一种监督式机器学习技术，用于预测类别或类型。在本模块中，您将学习如何使用 Azure 机器学习设计器创建分类模型。 4. 使用 Azure AI 创建聚类模型 - 描述：聚类是一种无监督学习技术，用于根据特征对相似实体进行分组。在本模块中，您将学习如何使用 Azure 机器学习设计器创建聚类模型。
Name:Use Automated Machine Learning in Azure Machine Learning
Description:Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this module, you'll learn how to identify different kinds of machine learning model and how to use the automated machine learning capability of Azure Machine Learning to train and deploy a predictive model.
Name:Create a Regression Model with Azure Machine Learning Designer
Description:Regression is a supervised machine learning technique used to predict numeric values. In this module, you will learn how to create regression models using Azure Machine Learning designer.
Name:Create a Classification Model with Azure AI
Description:Classification is a supervised machine learning technique used to predict categories or classes. In this module, you will learn how to create classification models using Azure Machine Learning designer.
Name:Create a Clustering Model with Azure AI
Description:Clustering is an unsupervised machine learning technique used to group similar entities based on their features. In this module, you will learn how to create clustering models using Azure Machine Learning designer.
Machine learning is at the core of artificial intelligence, and many modern applications and services depend on predictive machine learning models. Training a machine learning model is an iterative process that requires time and compute resources. Automated machine learning can help make it easier. In this course, you will learn how to use Azure Machine Learning to create and publish models without writing code. This is the second course in a five-course program that prepares you to take the DP-100: Designing and Implementing a Data Science Solution on Azurecertification exam. The certification exam is an opportunity to prove knowledge and expertise operate machine learning solutions at a cloud-scale using Azure Machine Learning. This specialization teaches you to leverage your existing knowledge of Python and machine learning to manage data ingestion and preparation, model training and deployment, and machine learning solution monitoring in Microsoft Azure. Each course teaches you the concepts and skills that are measured by the exam. This Specialization is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning solutions in the cloud. It teaches data scientists how to create end-to-end solutions in Microsoft Azure. Students will learn how to manage Azure resources for machine learning; run experiments and train models; deploy and operationalize machine learning solutions, and implement responsible machine learning. They will also learn to use Azure Databricks to explore, prepare, and model data; and integrate Databricks machine learning processes with Azure Machine Learning.
