数据挖掘入门到精通—R语言课程
课程纲要：第一章：基本概念介绍第1课、数据挖掘、R语言概念介绍第2课、软件安装和数据的读、写、修复第3课、基本概念解说（向量、矩阵、因子、数据框、列表）第4课、基本图形的解说和制作第二章：有效软件包介绍及使用第5课、plyr包主函数解说第6课、plyr包辅佐函数解说第7课、Ggpolt2介绍第8课、Ggpolt2实践第9课、reshape2包的解说和实际操作第10课、课缺失值的管理第三章：算法解说及使用第11课、knn原理简介第12课、knn算法实际操作第13课、决策树的理论解说第14课、决策树实操第15课、人工神经网络的介绍1第16课、人工神经网络介绍2第17课、人工神经网络实操1第18课、人工神经网络实操2第19课、支持向量机原理介绍第20课、支持向量机的实操(Course outline: Chapter 1: Introduction to Basic Concepts Lesson 1, Data Mining, R Language Concept Introduction Lesson 2, Software Installation and Data Reading, Writing, and Repairing Lesson 3, Introduction to Basic Concepts (Vector, Matrix, Factor, Data Frame, List) Lesson 4, Introduction and Production of Basic Graphics Chapter 2: Introduction and Use of Effective Software Packages Lesson 5, Introduction to the Main Functions of the plyr Package Lesson 6, Introduction to the Auxiliary Functions of the plyr Package Lesson 7, Introduction to the Ggpolt2 Lesson 8 Ggpolt2 Practice Lesson 9, Reshape2 Package Explanation and Practical Operation Lesson 10, Missing Value Management Chapter 3: Algorithm Explanation and Use Lesson 11, Introduction to knn Principle Lesson 12, knn Algorithm Practical Operation Lesson 13, Decision Tree Theory Explanation Lesson 14, Decision Tree Practical Operation Lesson 15, Artificial Neural Network Introduction Lesson 16, Artificial Neural Network Introduction Lesson 2, Artificial Neural Network Practice Lesson 17, Artificial Neural Network Practice Lesson 1, 18, Artificial Neural Network Practice Lesson 2, 19 Introduction to Principle of Support Vector Machine Lesson 20: Practice of Support Vector Machine)
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