(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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