This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. You will be exposed to various NLP and Machine Learning topics in this book, some of which are presented in a cursory manner for two reasons. First, it’s important that you be exposed to these concepts. In some cases, you will find topics that might pique your interest, and hence motivate you to learn more about them through self-study; in other cases, you will probably be satisfied with a brief introduction. Hence, you can decide whether to delve into more detail regarding the topics in this book. Second, a full treatment of all the topics that are covered in this book would probably triple its page count, and few people are interested in reading long technical books. Hence, this book provides a decent view of the NLP and Machine Learning landscape, based on the belief that this approach will be more beneficial for readers who are experienced developers who want to learn about NLP and Machine Learning. Specifically, you will learn about the transformer architecture, the pre-trained BERT model and its variants, and features of GPT-2 and GPT-3 from OpenAI. Please note that this chapter contains Python-based code samples.