|
所在平台: Coursera |
课程主页: https://www.coursera.org/learn/sas-viya-rest-api-python-r
课程评论：没有评论
课程名称：使用 SAS Viya REST API 与 Python 和 R 概述：SAS Viya 是一个内存分布式环境，旨在快速有效地分析大数据。在本课程中，您将学习如何使用 SAS Viya API，通过 Jupyter Notebook 使用 R 或 Python 来控制 SAS 云分析服务。您将学习如何将数据上传到云中，进行数据分析，并利用 SAS Viya 创建预测模型，使用 SWAT 包（SAS 脚本包装器，用于分析转移）访问熟悉的开源功能。课程还将教授如何创建机器学习和深度学习模型，以应对各种数据集和复杂问题。完成分析后，您将能够下载数据并使用本地开源语法进行结果比较和图形创建。 课程大纲： 1. 课程概述：介绍讲师和课程物流，包括如何访问课程所需软件。 2. SAS® Viya® 和开源集成：学习 SAS Viya 的分析处理引擎 Cloud Analytic Services 服务器，并了解如何通过开源语言 R 和 Python 向 SAS Viya 提交数据处理命令。 3. 机器学习：学习如何使用 R 和 Python 创建、优化和评估 SAS Viya 预测模型，并有效管理模型的创建和评估。 4. 文本分析：学习如何利用自然语言处理分析文本文件集合，并将非结构化文本转换为适合预测建模的数值输入。 5. 深度学习：学习深度学习方法如何扩展传统神经网络模型，以及如何使用 R 和 Python API 为 SAS Viya 创建递归神经网络模型以处理序列数据，如时间序列和文本字符串。 6. 时间序列：学习使用流行的预测方法（如指数平滑和 ARIMAX）建模时间序列，及如何通过 R 和 Python API 为 SAS Viya 创建预测。 7. 图像分类：学习卷积神经网络如何用于图像分类，并使用 R 和 Python API 为 SAS Viya 创建卷积神经网络。 8. 因式分解机：学习因式分解机如何用于创建推荐引擎，以及如何在 SAS Viya 中使用 R 和 Python API 构建因式分解机模型。
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:SAS® Viya® and Open Source Integration
Description:In this module you learn about the analytical processing engine behind SAS Viya, the Cloud Analytic Services server. You also learn how to submit data processing commands to SAS Viya from the open source languages R and Python.
Name:Machine Learning
Description:In this module you learn how to use R and Python to create, optimize, and assess SAS Viya predictive models. You also learn how to use R and Python to efficiently manage the creation and assessment of these models.
Name:Text Analytics
Description:In this module you learn how natural language processing is used to analyze collections of text documents. You also learn how to turn blocks of unstructured text into numeric inputs suitable for predictive modeling.
Name:Deep Learning
Description:In this module you learn how deep learning methods extend traditional neural network models with new options and architectures. You also learn how recurrent neural networks are used to model sequence data like time series and text strings, and how to create these models using R and Python APIs for SAS Viya.
Name:Time Series
Description:In this module you learn how to model time series using two popular methods, exponential smoothing and ARIMAX. You also learn how to use the R and Python APIs for SAS Viya to create forecasts using these classical methods and using recurrent neural networks for more complex problems.
Name:Image Classification
Description:In this module you learn how convolutional neural networks are used to classify images and how to use the R and Python APIs for SAS Viya to create convolutional neural networks.
Name:Factorization Machines
Description:In this module you learn how factorization machines are used to create recommendation engines and how to build factorization machine models in SAS Viya using the R and Python APIs.
SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.
