"Quick answers to common problems"--Cover
Includes index

Table of Contents
Preface
Chapter 1: Practical Machine Learning with R
Introduction
Downloading and installing R
Downloading and installing RStudio
Installing and loading packages
Reading and writing data
Using R to manipulate data
Applying basic statistics
Visualizing data
Getting a dataset for machine learning
Chapter 2: Data Exploration with RMS Titanic
Introduction
Reading a Titanic dataset from a CSV file Converting types on character variablesDetecting missing values
Imputing missing values
Exploring and visualizing data
Predicting passenger survival with a decision tree
Validating the power of prediction with a confusion matrix
Assessing performance with the ROC curve
Chapter 3: R and Statistics
Introduction
Understanding data sampling in R
Operating a probability distribution in R
Working with univariate descriptive statistics in R
Performing correlations and multivariate analysis Operating linear regression and multivariate analysisConducting an exact binomial test
Performing student's t-test
Performing the Kolmogorov-Smirnov test
Understanding the Wilcoxon Rank Sum and Signed Rank test
Working with Pearson's Chi-squared test
Conducting a one-way ANOVA
Performing a two-way ANOVA
Chapter 4: Understanding Regression Analysis
Introduction
Fitting a linear regression model with lm
Summarizing linear model fits
Using linear regression to predict unknown values
Generating a diagnostic plot of a fitted model ""Fitting a polynomial regression model with lm""""Fitting a robust linear regression model with rlm""; ""Studying a case of linear regression on SLID data""; ""Applying the Gaussian model for generalized linear regression""; ""Applying the Poisson model for generalized linear regression""; ""Applying the Binomial model for generalized linear regression""; ""Fitting a generalized additive model to data""; ""Visualizing a generalized additive model""; ""Diagnosing a generalized additive model""; ""Chapter 5: Classification (I) � Tree, Lazy, and Probabilistic""; ""Introduction"" Preparing the training and testing datasetsBuilding a classification model with recursive partitioning trees
Visualizing a recursive partitioning tree
Measuring the prediction performance of a recursive partitioning tree
Pruning a recursive partitioning tree
Building a classification model with a conditional inference tree
Visualizing a conditional inference tree
Measuring the prediction performance of a conditional inference tree
Classifying data with the k-nearest neighbor classifier
Classifying data with logistic regression

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.