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所在平台: Coursera |
课程主页: https://www.coursera.org/learn/cluster-analysis-association-mining-and-model-evaluation
课程评论：没有评论
课程名称：聚类分析、关联挖掘与模型评估 课程概述：欢迎参加《聚类分析、关联挖掘与模型评估》课程。本课程将探索聚类分析和分割，并讨论如何应用协同过滤和关联规则挖掘等技术。我们还将解释如何评估模型的性能，并回顾分析类型的差异及其适用场景。 课程大纲： 1. **聚类分析与分割** - 描述：在第一模块中，我们将探索聚类分析，这是一种流行的无监督学习算法。我们还将回顾聚类分析的两种主要风格，并讨论其在各个行业中的潜在应用。 2. **协同过滤、关联规则挖掘（市场篮子分析）** - 描述：在第二模块中，我们将解释协同过滤和关联规则挖掘，并讨论这些技术如何用于自动预测。我们还将深入了解市场篮子分析的各种常见应用。 3. **分类类型预测模型** - 描述：在第三模块中，我们将解释如何评估分类类型预测模型的性能，以及混淆矩阵如何帮助可视化这些性能。我们还将讨论聚类分析的适用性，以及如何使用它来检测诸如欺诈交易等稀有事件。 4. **回归类型预测模型** - 描述：在第四模块中，我们将回顾回归分析如何用于假设检验和预测，并探讨如何利用散点图更好地理解两个变量之间的关系。我们还将讨论相关分析与回归分析之间的区别，以及简单回归与多重回归的对比。 本课程结合理论与实践，适合希望深入了解数据分析基础的学习者。
Name:Cluster Analysis and Segmentation
Description:Welcome to Module 1, Cluster Analysis and Segmentation. In this module we will explore cluster analysis, a popular unsupervised learning algorithm. We will also review the two major styles of cluster analysis, and discuss potential applications to different industries.
Name:Collaborative Filtering, Association Rules Mining (Market Basked Analysis)
Description:Welcome to Module 2, Collaborative Filtering, Association Rules Mining, & Market Basket Analysis. In this module we will begin with an explanation of collaborative filtering and association rules mining, and how these techniques are used to make automatic predictions. We will also take a closer look at the various common applications of market basket analysis.
Name:Classification-Type Prediction Models
Description:Welcome to Module 3, Classification-Type Prediction Models. In this module we will begin with an explanation of how classification-type prediction models are evaluated for performance, and how a confusion matrix can help visualize that performance. We will also discuss the applicability of cluster analysis, and how it can be used to detect rare events such as fraudulent transactions.
Name:Regression-Type Prediction Models
Description:Welcome to Module 4, Regression-Type Prediction Models. In this module we will review how regression analytics are used for both hypothesis testing and prediction, and how a scatter plot can be leveraged to better understand the relationship between two variables. We will also discuss the differences between correlation analysis and a regression analysis, and a look at simple vs multiple regression.
Welcome to Cluster Analysis, Association Mining, and Model Evaluation. In this course we will begin with an exploration of cluster analysis and segmentation, and discuss how techniques such as collaborative filtering and association rules mining can be applied. We will also explain how a model can be evaluated for performance, and review the differences in analysis types and when to apply them.
