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A Textbook-Based Approach To MACHINE LEARNING
Machine learning is best learned with clear structure, definitions, evaluation, and hands-on work. This book takes a textbook-based approach to machine learning, moving from first principles to practice using Python with scikit-learn, equipping readers with a clear, practical grasp of the field.
Starting with what machine learning is, the book builds a firm data representation, learning paradigms, feature types, model selection, training, evaluation, cross-validation, generalization, and the bias–variance trade-off. Chapters 1–3 establish the theoretical foundations of these topics, while Chapters 4–11 integrate theory with practical, hands-on work in Python with scikit-learn. The book proceeds through supervised learning (classification and regression) and unsupervised learning (clustering), and concludes with artificial neural networks and their learning rules. Each chapter combines mathematical formulations with practical examples, includes illustrative figures, tables, and runnable code.
Key Features
• Preprocessing and feature a full chapter on data preprocessing and feature engineering—cleaning, integration, reduction, transformation, resampling, extraction, construction, selection, scaling, and encoding – so that models are trained on well-formed data.
• Clear predictive vs descriptive; parametric vs non-parametric; discriminative vs generative; geometric, probabilistic and rule-based models with clear explanations.
• Comprehensive model supervised (k-NN, decision trees, ensembles, Naïve Bayes, SVM, etc.) and unsupervised (k-means, hierarchical, etc.) learning, with selection guidelines, key hyperparameters, and common trade-offs.
• From theory to illustrative Python (scikit-learn) examples for k-NN, decision trees, random forests, gradient boosting, Naïve Bayes, SVMs, K-Means etc.—easy to tailor to other datasets.
• chapter-end review questions, true/false, and MCQs with answers at the end of each chapter.
Who should read this book
Practitioners working in the field of machine learning and data science — undergraduate and graduate students in computer science, AI, data science, and related programs; engineers and analysts transitioning to machine learning; and instructors seeking a course-ready, implementation-oriented text with a clean progression from fundamentals to applications.
Starting with what machine learning is, the book builds a firm data representation, learning paradigms, feature types, model selection, training, evaluation, cross-validation, generalization, and the bias–variance trade-off. Chapters 1–3 establish the theoretical foundations of these topics, while Chapters 4–11 integrate theory with practical, hands-on work in Python with scikit-learn. The book proceeds through supervised learning (classification and regression) and unsupervised learning (clustering), and concludes with artificial neural networks and their learning rules. Each chapter combines mathematical formulations with practical examples, includes illustrative figures, tables, and runnable code.
Key Features
• Preprocessing and feature a full chapter on data preprocessing and feature engineering—cleaning, integration, reduction, transformation, resampling, extraction, construction, selection, scaling, and encoding – so that models are trained on well-formed data.
• Clear predictive vs descriptive; parametric vs non-parametric; discriminative vs generative; geometric, probabilistic and rule-based models with clear explanations.
• Comprehensive model supervised (k-NN, decision trees, ensembles, Naïve Bayes, SVM, etc.) and unsupervised (k-means, hierarchical, etc.) learning, with selection guidelines, key hyperparameters, and common trade-offs.
• From theory to illustrative Python (scikit-learn) examples for k-NN, decision trees, random forests, gradient boosting, Naïve Bayes, SVMs, K-Means etc.—easy to tailor to other datasets.
• chapter-end review questions, true/false, and MCQs with answers at the end of each chapter.
Who should read this book
Practitioners working in the field of machine learning and data science — undergraduate and graduate students in computer science, AI, data science, and related programs; engineers and analysts transitioning to machine learning; and instructors seeking a course-ready, implementation-oriented text with a clean progression from fundamentals to applications.
444 pages, Paperback
Published November 19, 2025
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