{"product_id":"python-programming-machine-learning-data-science-scikit-learn-linear-regression-logistic-regression-knn-cross-validation-grid-decision-tree-9798231631063","title":"Python Programming: Machine Learning \u0026 Data Science, Scikit-learn (Linear Regression, Logistic Regression, KNN, Cross-Validation, Grid, Decision Tree,","description":"\u003cp\u003e • Author(s): E3\u003cbr\u003e • Publisher: E3\u003cbr\u003e • Publisher Imprint: E3\u003cbr\u003e • BISAC: Languages - Python\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/li\u003e\n\u003cli\u003eThe 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, \u003cem\u003eMachine Learning \u0026amp; Data Science: Scikit-learn\u003c\/em\u003e 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: \u003cstrong\u003eScikit-learn\u003c\/strong\u003e.\u003c\/li\u003e\n\u003cli\u003eThis 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 \u003cem\u003ehow\u003c\/em\u003e an algorithm works, but \u003cem\u003ewhy\u003c\/em\u003e it behaves the way it does, and \u003cem\u003ewhen\u003c\/em\u003e to apply it effectively.\u003c\/li\u003e\n\u003cli\u003eWe begin with core algorithms such as \u003cstrong\u003eLinear Regression\u003c\/strong\u003e, \u003cstrong\u003eLogistic Regression\u003c\/strong\u003e, and \u003cstrong\u003eK-Nearest Neighbors (KNN)\u003c\/strong\u003e-laying the groundwork for predictive modeling and classification tasks. Next, we introduce model validation techniques like \u003cstrong\u003eCross-Validation\u003c\/strong\u003e and \u003cstrong\u003eGrid Search\u003c\/strong\u003e, essential tools for evaluating and optimizing model performance. Building upon this foundation, we explore more complex algorithms like \u003cstrong\u003eDecision Trees\u003c\/strong\u003e and \u003cstrong\u003eSupport Vector Machines (SVMs)\u003c\/strong\u003e, which offer greater flexibility and power in modeling nonlinear patterns.\u003c\/li\u003e\n\u003cli\u003eAdditionally, we highlight the importance of \u003cstrong\u003efeature scaling\u003c\/strong\u003e techniques like \u003cstrong\u003eMin-Max normalization\u003c\/strong\u003e, 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.\u003c\/li\u003e\n\u003cli\u003eThroughout this book, we rely on \u003cstrong\u003eScikit-learn\u003c\/strong\u003e 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.\u003c\/li\u003e\n\u003cli\u003eWhether 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.\u003c\/li\u003e\n\u003cli\u003e\n\u003cstrong\u003e- \u003c\/strong\u003e\u003cem\u003e\u003cstrong\u003eThe Author\u003c\/strong\u003e\u003c\/em\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e","brand":"Atlantic Books","offers":[{"title":"Paperback","offer_id":46398001119383,"sku":"9798231631063","price":9168.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798231631063.webp?v=1769042887","url":"https:\/\/atlanticbooks.com\/products\/python-programming-machine-learning-data-science-scikit-learn-linear-regression-logistic-regression-knn-cross-validation-grid-decision-tree-9798231631063","provider":"Atlantic Books","version":"1.0","type":"link"}