Book Description The explosive growth of data in recent decades has transformed how we perceive problems, make decisions, and build intelligent systems. As industries across the globe embrace digital transformation, the demand for tools and techniques to extract meaningful insights from data has never been greater. This book, Machine Learning & Data Science: Scikit-learn is born out of that growing need-a practical, focused guide to foundational machine learning algorithms and their implementation using one of the most widely adopted libraries in Python: Scikit-learn. This book is designed for students, professionals, and enthusiasts seeking to build a strong conceptual and practical understanding of key machine learning techniques. Rather than overwhelming the reader with theory, we take a hands-on, example-driven approach centered on real-world applications and reproducible code. Each chapter builds from the ground up-explaining not just how an algorithm works, but why it behaves the way it does, and when to apply it effectively. We begin with core algorithms such as Linear Regression, Logistic Regression, and K-Nearest Neighbors (KNN)-laying the groundwork for predictive modeling and classification tasks. Next, we introduce model validation techniques like Cross-Validation and Grid Search, essential tools for evaluating and optimizing model performance. Building upon this foundation, we explore more complex algorithms like Decision Trees and Support Vector Machines (SVMs), which offer greater flexibility and power in modeling nonlinear patterns. Additionally, we highlight the importance of feature scaling techniques like Min-Max normalization, which often determine the success of machine learning models. These techniques, though sometimes overlooked, are vital for ensuring that algorithms perform as expected and generalize well to unseen data. Throughout this book, we rely on Scikit-learn for implementation-not only because of its simplicity and power, but also because it exemplifies best practices in structuring machine learning workflows. Readers will gain practical experience with the tools and pipelines used by data scientists and machine learning practitioners in real projects. Whether you are taking your first steps into machine learning or looking to deepen your understanding of algorithmic foundations, this book provides a concise and reliable guide. May it serve as both a roadmap and a reference for your journey into the fascinating world of machine learning and data science. - The Author