|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/market-basket-analysis-linear-discriminant-analysis-with-r/
课程评论：没有评论
课程名称：使用R进行市场篮子分析与线性判别分析 课程概述：本课程分为两个部分。第一部分解释了关联规则（市场篮子分析）。第二部分介绍了线性判别分析（LDA）。 第一部分 - 关联规则/市场篮子分析（MBA）详细内容： - 什么是市场篮子分析（MBA）或关联规则 - 关联规则的应用 - 如何在多种情况下应用 - 关联规则的结构，及其强度的衡量，包括支持度、置信度和提升度 - 归纳规则的基本算法 - 与基本算法的演示，讨论广度优先算法和深度优先算法 - 使用R的演示 - 两个案例 - 通过作业巩固所学概念 第二部分 - 线性判别分析（LDA）详细内容： - 分类模型的必要性 - 线性判别的目的 - 分类的用例 - LDA的正式定义及其分析技术的适用性 - LDA的两种用途： 1. 变量选择的LDA - 使用LDA进行变量选择的演示 2. LDA用于分类 - 理解LDA的三个重要组成部分，并分解LDA的复杂性： - 首个复杂性：测量距离 - 欧几里得距离 - 第二个复杂性：增强的测量距离 - 马哈拉诺比斯距离 - 第三个复杂性：线性判别函数与后验概率/贝叶斯定理 - 使用R进行LDA的演示，包括镊子法 - 深入分析LDA输出及其可视化操作 - 比较LDA与PCA - LDA应用于多于两个类别的情况的演示 - 数据可视化 - 模型开发和对训练数据集和测试数据集的验证 - 分类算法在行业中的应用 - 处理LDA中的特殊情况 本课程通过实例和实际演示帮助学员掌握市场篮子分析和线性判别分析的理论知识与应用技能。
This course has two parts. In part 1 Association rules (Market Basket Analysis) is explained. In Part 2, Linear Discriminant Analysis (LDA) is explained. L ------------------------- Details of Part 1 - Association Rules / Market Basket Analysis (MBA) -------------------------- What is Market Basket Analysis (MBA) or Association rulesUsage of Association Rules - How it can be applied in a variety of situations How does an association rule look like?Strength of an association rule - Support measureConfidence measure Lift measureBasic Algorithm to derive rulesDemo of Basic Algorithm to derive rules - discussion on breadth first algorithm and depth first algorithmDemo Using R - two examplesAssignment to fortify concepts ------------------------- Details of Part 2 - Linear (Market Basket Analysis) -------------------------- Need of a classification modelPurpose of Linear DiscriminantA use case for classificationFormal definition of LDAAnalytics techniques applicability Two usage of LDA LDA for Variable Selection Demo of using LDA for Variable Selection Second usage of LDA - LDA for classification Details on second practical usage of LDAUnderstand which are three important component to understand LDA properlyFirst complexity of LDA - measure distance:Euclidean distance First complexity of LDA - measure distance enhanced :Mahalanobis distanceSecond complexity of LDA - Linear Discriminant functionThird complexity of LDA - posterior probability / Bays theorem Demo of LDA using RAlong with jack knife approachDeep dive into LDA outputnVisualization of LDA operationsUnderstand the LDA chart statistics LDA vs PCA side by sideDemo of LDA for more than two classes: understandData visualizationModel developmentModel validation on train data set and test data setsIndustry usage of classification algorithm Handling Special Cases in LDA
