Preface

Data in today’s world is the new black gold which is growing exponentially. This growth can be attributed to the growth of existing data, and new data in a structured and unstructured format from multiple sources such as social media, Internet, documents and the Internet of Things. The flow of data must be collected, processed, analyzed, and finally presented in real time to ensure that the consumers of the data are able to take informed decisions in today’s fast-changing environment. Machine learning techniques are applied to the data using the context of the problem to be solved to ensure that fast arriving and complex data can be analyzed in a scientific manner using statistical techniques. Using machine learning algorithms that iteratively learn from data, hidden patterns can be discovered. The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt and learn to produce reliable decisions from new data sets.

We will start by introducing the various topics of machine learning, that will be covered in the book. Based on real-world challenges, we explore each of the topics under various chapters, such as Classification, Clustering, Model Selection and Regularization, Nonlinearity, Supervised Learning, Unsupervised Learning, Reinforcement Learning, Structured Prediction, Neural Networks, Deep Learning, and finally the case studies. The algorithms have been developed using R as the programming language. This book is friendly for beginners in R, but familiarity with R programming would certainly be helpful for playing around with the code.

You will learn how to make informed decisions about the type of algorithms you need to use and how to implement these algorithms to get the best possible results. If you want to build versatile applications that can make sense of images, text, speech, or some other form of data, this book on machine learning will definitely come to your rescue!