Machine Learning: An Introductory Handbook Using R

author: Dr. Karen Mazidi

Materials to accompany the book. A sample chapter and link to the book is available on my blog: karenmazidi.blogspot.com

Table of Contents

• Part 1 Introduction to Machine Learning and R
  • 1 - The Craft of Machine Learning
  • 2 - Learning R
  • 3 - Data Visualization in R
• Part 2 Linear Models
  • 4 - Linear Regression
  • 5 - Logistic Regression
  • 6 - Naive Bayes
  • 7 - SVM
  • 8 - The Craft 2: Data Wrangling
• Part 3 Searching for Similarity
  • 9 - Instance-Based Learning with kNN
  • 10 - Clustering
  • 11 - Decision Trees and Random Forests
  • 12 - The Craft 3: Feature Engineering
• Part 4 Neural Networks
  • 13 - Neural Networks
  • 14 - Deep Learning
  • 15 - The Craft 4: Never Stop Learning
• Part 5 Modeling the World
  • 16 - Bayes Nets
  • 17 - Markov Models
  • 18 - Reinforcement Learning
  • 19 - The Craft 5: Algorithms
• Part 6 Appendices
  • 20 - Modern R
  • 21 - Big Data with R
  • 22 - Sampling Big Data