Harness the power of R to build common machine learning algorithms with realworld data science applications
Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results
Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems
Classify your data with Bayesian and nearest neighbour methods
Predict values using R to build decision trees, rules, and support vector machines
Forecast numeric values with linear regression and model your data with neural networks
Evaluate and improve the performance of machine learning models
Learn specialized machine learning techniques for text mining, social network data, and big data
Who this book is for
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
Table of Contents
Introducing Machine Learning
Managing and Understanding Data
Lazy Learning: Classification using Nearest Neighbors
Probabilistic Learning: Classification using Naïve Bayes
Divide and Conquer: Classification using Trees and Rules
Forecasting Numeric Data: Regression Methods
Black Box Methods: Neural Networks and Support Vector Machines
Finding Patterns: Market Basket Analysis Using Association Rules