Learning from Data made easy with R. A gentle Introduction for Data Science

Description

Contents......Page 2 1 The Learning Problem in a Nutshell......Page 13 The ABCs of Inductive and Deductive Inference......Page 14 Three Key Elements of the Learning Problem......Page 17 The Goal of Learning from Data......Page 19 Notes......Page 23 2 Supervised Learning......Page 25 The Essential Ingredient for Effective Classification......Page 26 The Answer to How to Determine the Hypothesis Class......Page 27 The Two Core Approaches to Super- vised Learning......Page 29 What is a Bayes Classifier?......Page 30 Two Easy Tricks to Evaluate the Bayes Error......Page 33 How to Unleash the Power of the Naive Bayes Classifier......Page 35 Incredibly Simply Ways to Build the Naive Bayes Classifier in R......Page 39 How to Leverage the Value of the k- Nearest Neighbors Algorithm......Page 49 A Straightforward Way to Use k- Nearest Neighbors in R......Page 53 The Key to Linear Discriminant Anal- ysis......Page 60 Essential Elements for Discriminant Analysis in R......Page 63 The Secret of Classification with Lo- gistic Regression......Page 69 The Easy Way to Build a Logistic Re- gression Classifier in R......Page 72 A Damn Good Idea to Inspire Your Creativity and Passion......Page 78 Notes......Page 81 Unsupervised Learning in a Nutshell......Page 87 The Two Core Approaches and How They Work......Page 88 Techniques that Crush Unsupervised Learning & How to Build them in R......Page 89 A Fantastic Winning Example of Un- supervised Learning You Can Emu- late......Page 107 Notes......Page 112 4 Semi Supervised Learning......Page 114 How Can Unlabeled Data Can Help?......Page 115 The Consistency Assumption......Page 117 A Super Simple Way to Try Semi- supervised Learning......Page 118 The Self Learning Algorithm......Page 119 Semi-Supervised Model Based Learn- ing with R......Page 122 Master this Practical Illustration us- ing Land Classification......Page 127 Notes......Page 132 5 Statistical Learning Theory......Page 134 The Vapnik-Chervonenkis Generaliza- tion Bound......Page 135 What is the Vapnik-Chervonenkis Di- mension?......Page 137 The Key to Structural Risk Minimiza- tion......Page 140 The Best Advice on Using Statistical Learning Theory in Practice......Page 141 How to Master the Support Vector Machine......Page 143 Notes......Page 150 6 Model Selection......Page 151 How I Improved My Models in One Evening......Page 152 A Little Mistake that Cost $5 Million......Page 153 Three Key Lessons of the No Free Lunch Theorem......Page 155 What is the Bias Variance Trade-off?......Page 158 Do You Make This Mistake with Your Models?......Page 162 The Secret of the Hold Out Technique......Page 164 The Art of Effective Cross Validation......Page 165 Notes......Page 174 Index......Page 176