Preface

You have already seen Harvard Business Review describing data science as the sexiest job of the 21^st century. You have been watching terms such as machine learning and artificial intelligence pop up around you in the news all the time. You aspire to join this league of machine learning data scientists soon. Or maybe, you are already in the field but want to take your career to the next level. You want to learn more about the underlying statistical and mathematical theory, and apply this new knowledge using the most commonly used tool among practitioners, scikit-learn.

This book is here for you. It begins with an explanation of machine learning concepts and fundamentals and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms and shows you how to use them to solve real-life problems. You'll also learn various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you'll gain a thorough understanding of its theory and learn when to apply it to real-life problems.

This book will not stop at scikit-learn, but will help you add even more tools to your toolbox. You will augment scikit-learn with other tools such as pandas, Matplotlib, imbalanced-learn, and scikit-surprise. By the end of this book, you will be able to orchestrate these tools together to take a data-driven approach to providing end-to-end machine learning solutions.