Preface

Although the technology world today is all abuzz about artificial intelligence (AI) and the large language models (LLMs) that power them, machine learning (ML) is still providing value to businesses through predictive modeling and prescriptive analytics. So many systems today are powered by ML on the backend that most people would be surprised to learn how often businesses employ such techniques to refine their marketing strategy, upsell and improve product placement, and customize user experiences, among other applications.

While countless tools and software exist today to enable ML applications, one tool has become the backbone of both hobbyists and enterprises alike: scikit-learn. It’s hard to believe that scikit-learn v0.1 debuted over 15 years ago in January 2010, yet even after all that time and all the changes and advancements in ML and AI, it still holds its place as one of the foremost Python libraries for both AI/ML.

scikit-learn is a powerful, open source ML library for Python that provides simple and efficient tools for data mining and data analysis, built on top of NumPy, SciPy, and Matplotlib. It offers a versatile, user-friendly framework for implementing a wide range of ML algorithms, enabling efficient development and deployment of predictive models in real-world applications.

This book is devoted to scikit-learn v1.5. It takes you on a journey from understanding the fundamentals of ML and data preprocessing, through implementing advanced algorithms and techniques, to deploying and optimizing ML models in production. Along the way, you will explore practical, step-by-step recipes that cover everything from feature engineering and model selection to hyperparameter tuning and model evaluation, all using scikit-learn 1.5.

Finally, every chapter contains examples designed to give you an opportunity to apply the chapter’s learning through coding exercises.