You are cordially invited to attend the event titled “Machine Learning for Healthcare: Predictive Analytics and Personalised Medicine.” We would like to use this opportunity to introduce ourselves. The purpose of this book was to provide readers who are interested in the confluence of machine learning, healthcare, predictive analytics, and personalised medicine with a comprehensive resource that covers all aspects of the subject matter. Whether they are healthcare professionals, researchers, students, or industry practitioners, we feel that this book will provide readers with valuable insights into the application of machine learning in healthcare settings. This is true regardless of the reader’s occupation. In the past several years, the healthcare industry has seen a substantial transformation as a result of the advancements in technology and the availability of large amounts of medical data. Together, these two elements have been responsible for bringing about this transition. Machine learning has emerged as a valuable technology that can be utilised by researchers as well as professionals working in the field of medicine. This might be attributed to the capability of machine learning to analyse complex data in order to identify patterns and insights. We are able to precisely forecast results, give individualised treatment choices, and extract important information from large-scale patient datasets with the assistance of this technology. The organisation of this book is intended to accomplish precisely that, and it will guide you through the fundamental ideas and applications of machine learning in the healthcare industry. It is our intention to examine a wide range of machine learning algorithms, as well as methods for data preparation, feature selection, model evaluation, and validation procedures, within the context of healthcare applications. In addition, we will study the challenges and ethical concerns that are associated with the application of machine learning in the field of healthcare. There are many different topics that are discussed in this book, some of which include the following: data preparation, feature engineering, supervised and unsupervised learning approaches, deep learning, natural language processing, and many more. It provides a comprehensive understanding of machine learning as well as the ways in which such techniques might be applied in the field of medicine. There are many different applications of machine learning in the field of medicine, and some of these applications are addressed throughout the entirety of this book. These applications include the diagnosis and prediction of illnesses, the development of drugs, genomics, the analysis of medical imaging, the analysis of electronic health information, and the creation of individualised treatment recommendation systems.