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TRANSCRIPT
Chapter 1: A Process for Success
Chapter 2:Linear Regression – The Blocking and Tackling of Machine Learning
Chapter 3: Logistic Regression and Discriminant Analysis
Chapter 4: Advanced Feature Selection in Linear Models
Chapter 5: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines
Chapter 6: Classification and Regression Trees
Chapter 7: Neural Networks
Chapter 8, Cluster Analysis
Chapter 9: Principal Components Analysis
Chapter 10: Market Basket Analysis and Recommendation Engines
Chapter 11: Time Series and Causality
Chapter 12: Text Mining
Appendix: R Fundamentals
Preface