|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/machine-learning-sas
课程评论：没有评论
课程名称：使用SAS Viya进行机器学习 课程概述：本课程涵盖了与监督机器学习模型相关的不同技术的理论基础。此外，通过一个商业案例研究，引导参与者完成分析生命周期的各个步骤，包括问题理解、模型部署、数据准备、特征选择、模型训练与验证以及模型评估。课程中使用一系列演示和练习来巩固概念和解决商业问题的分析方法。本课程使用SAS Viya中的Model Studio，该界面支持您准备、开发、比较和部署先进的分析模型。您将学习如何训练监督机器学习模型，以便在大数据上做出更好的决策。本课程中使用的SAS应用使得机器学习可以在无需编程或编码的情况下进行。 课程大纲： 1. 课程概述：认识讲师并了解课程的后勤信息，比如如何访问课程所需软件。 2. 机器学习与SAS® Viya®入门：学习如何使用SAS® Viya®应对当今的商业挑战，并开始进行贯穿整个课程的项目。 3. 数据预处理与算法选择：探索数据并完成数据分析准备，同时学习一些选择算法的一般考虑因素。 4. 决策树与树的集成：构建决策树模型及基于决策树组合的模型。 5. 神经网络：学习构建神经网络模型。 6. 支持向量机：学习构建支持向量机模型。 7. 模型评估与部署：选择满足商业挑战要求的最佳模型并将其投入生产，同时学习如何管理模型。 8. 附加节点：将提供补充内容。 9. 认证实践考试：为认证评估提供练习。 该课程旨在帮助学习者掌握机器学习的基本技能，以便在实际商业环境中应用数据分析解决复杂问题。
Name:Course Overview
Description:In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
Name:Getting Started with Machine Learning and SAS® Viya
Description:In this module, you learn how you can meet today's business challenges with machine learning using SAS® Viya®. You start working on the project that runs throughout the course.
Name:Data Preprocessing and Algorithm Selection
Description:In this module, you learn to explore the data and finish preparing the data for analysis. You also learn some general considerations for selecting an algorithm.
Name:Decision Trees and Ensembles of Trees
Description:In this module, you learn to build decision tree models as well as models based on ensembles, or combinations, of decision trees.
Name:Neural Networks
Description:In this module, you learn to build neural network models.
Name:Support Vector Machines
Description:In this module, you learn to build support vector machine models.
Name:Model Assessment and Deployment
Description:In this module, you learn how to select the model that best meets the requirements of your business challenge and put the model into production. You also learn about managing the model over time.
Name:Additional Nodes
Description:
Name:Certification Practice Exam
Description:
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. A series of demonstrations and exercises is used to reinforce the concepts and the analytical approach to solving business problems. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.
