This introductory chapter will get you started with the basics of R which include various constructs, useful data structures, loops and vectorization. If you are already an R wizard, you can skim through these sections and dive right into the next part which talks about what machine learning actually represents as a domain and the main areas it encompasses. We will also talk about different machine learning techniques and algorithms used in each area. Finally, we will conclude by looking at some of the most popular machine learning packages in R, some of which we will be using in the subsequent chapters.

If you are a data or machine learning enthusiast, surely you would have heard by now that being a data scientist is referred to as the sexiest job of the 21^st century by Harvard Business Review.

There is a huge demand in the current market for data scientists, primarily because their main job is to gather crucial insights and information from both unstructured and structured data to help their business and organization grow strategically.

Some of you might be wondering how machine learning or R relate to all this! Well, to be a successful data scientist, one of the major tools you need in your toolbox is a powerful language capable of performing complex statistical calculations and working with various types of data and building models which help you get previously unknown insights and R is the perfect language for that! Machine learning forms the foundation of the skills you need to build to become a data analyst or data scientist, this includes using various techniques to build models to get insights from data.

This book will provide you with some of the essential tools you need to be well versed with both R and machine learning by not only looking at concepts but also applying those concepts in real-world examples. Enough talk; now let's get started on our journey into the world of machine learning with R!

In this chapter, we will cover the following aspects: