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.
