Chapter 1, Transforming Data into Actions, shows you how new technologies allow you to solve business problems with a data-driven approach.

Chapter 2, R – A Powerful Tool for Developing Machine Learning Algorithms, explains why R is a great option for machine learning, and covers the basics of the software.

Chapter 3, A Simple Machine Learning Analysis, shows you a simple example of machine learning solutions.

Chapter 4, Step 1 – Data Exploration and Feature Engineering, shows you how to clean and transform the data before using machine learning algorithms.

Chapter 5, Step 2 – Applying Machine Learning Techniques, shows you how to apply machine learning algorithms to solve the problem.

Chapter 6, Step 3 – Validating the Results, shows you how to measure an algorithm's accuracy in order to tune its parameters.

Chapter 7, Overview of Machine Learning Techniques, presents the main branches of machine learning algorithms.

Chapter 8, Machine Learning Examples Applicable to Businesses, shows you how to solve a business problem using machine learning.

The only software that you need to run the code is R, preferably 3.0.0+. It is highly recommended, although not necessary, that you install the RStudio Desktop IDE.

This book is intended for those who want to learn how to perform some machine learning using R, in order to gain insight from their data and to find the solution to some real-life problems. 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.

In this book, you will find a number of text styles that distinguish between different kinds of information. Here are some examples of these styles and an explanation of their meaning.

Code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles are shown as follows: "Load the randomForest package containing the random forest algorithm."

A block of code is set as follows:

[default]
arrayFeatures <- names(dtBank)
arrayFeatures <- arrayFeatures[arrayFeatures != 'output']
formulaAll <- paste('output', '~')
formulaAll <- paste(formulaAll, arrayFeatures[1])
for(nameFeature in arrayFeatures[-1]){
  formulaAll <- paste(formulaAll, '+', nameFeature)
}
formulaAll <- formula(formulaAll)

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

n1 + n2
[1] 5
n1 * n2
[1] 6

New terms and important words are shown in bold.

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