Business Machine Learning

This book offers an introduction to the foundations of machine learning (ML) tailored specifically for non-technical readers. Designed to bridge the gap between technical concepts and real-world business applications, this textbook equips readers with the analytical skills needed to thrive in an increasingly data-driven landscape. Readers are expected to have a working familiarity with Python and data preprocessing. Those looking to build this foundation can first explore our companion text, Business Data Analytics.

To foster active, applied learning, each chapter integrates: Concept Checks: Embedded multiple-choice questions to reinforce key ideas as you progress. Critical Discussions: Debate prompts and open-ended questions that encourage deeper analysis of ML's business and ethical implications. Hands-On Exercises: Practical coding tasks that connect theory directly to real-world operations and strategic decision-making.

Core topics include Na¯ve Bayes, Random Forests, Logistic Regression, Linear/Tree/Forest Regression, PCA, K-Means Clustering, and Support Vector Machines (SVM). Each module follows a consistent, practice-oriented structure: clear conceptual explanations, step-by-step Python implementations, and guided interpretation of results through actionable business narratives.

By balancing foundational theory with practical application, this book ensures readers not only understand essential algorithms but also learn how to translate model outputs into strategic business insights. Upon completion, readers will be well-prepared to navigate, implement, and lead ML-driven initiatives in professional settings.

A Note on Code Formatting: Due to print layout constraints, some code lines may wrap to the next line without explicit continuation markers. Readers may need to manually rejoin broken lines when transcribing code for execution.