Chapter 1, Practical Machine Learning with R, shows how to install and setup R environment, it covers package installation basic syntax and data types followed by reading and writing data from various sources. It also covers basic statistics and visualization using R.
Chapter 2, Data Exploration with Air Quality Datasets, shows how actual data looks in R. It covers loading of data, exploring and visualizing the data.
Chapter 3, Analyzing Time Series Data, shows a totally different type of data which consist of time factor. It covers how to handle time series in R.
Chapter 4, R and Statistics, covers data sampling, probability distribution, univariate descriptive statistics, correlation, multivariate analysis, linear regression. Exact binomial test, student – t test, Kolmogorov-Smirnov test, Wilcoxon Rank Sum and Signed Rank test, Pearson's Chi-squared Test, One-way ANOVA, and Two-way ANOVA.
Chapter 5, Understanding Regression Analysis, introduces to the supervised learning, to analyze the relationship between dependent and independent variable. It covers different type of distribution model followed by generalized additive model.
Chapter 6, Survival Analysis, shows how to analyze the data where the outcome variable is time for occurrence of an event, widely used in clinical trials.
Chapter 7, Classification 1 – Tree, Lazy and Probabilistic, Tree, Lazy and Probabilistic, deals with classification model built from the training dataset, of which the categories are already known.
Chapter 8, Classification 2 – Neural Network and SVM, shows how to train a support vector machine and neural network, how to visualize and tune the both.
Chapter 9, Model Evaluation, shows to evaluate the performance of a fitted model.
Chapter 10, Ensemble Learning, shows bagging and boosting to classify the data, perform the cross validation to estimate the error rate. It also covers the random forest.
Chapter 11, Clustering, means grouping similar objects widely used in business applications. It covers four clustering techniques, validating clusters internally.
Chapter 12, Association Analysis and Sequence Mining, covers finding the hidden relationships within a transaction data set. It shows how to create and inspect the transaction data set, performing association analysis with an Aprori algorithm, visualizing associations in various graphs formats, using Eclat algorithm finding frequent itemset.
Chapter 13, Dimension Reduction, shows how to deal with redundant data and removing irrelevant data. It shows how to perform feature ranking and selection, extraction and dimension reduction using linear and nonlinear methods.
Chapter 14, Big Data Analysis ( R and Hadoop ), shows how R can be used with big data. It covers preparing of Hadoop environment, performing MapReduce from R, operate a HDFS, performing common data operation.
