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Classical Machine Learning [Aburass] - mklabgr - 08-20-2026 Book:Classical Machine Learning: A Practical Guide Using Python Authors: Sanad Aburass & Ibrahim Aljarah Publication date: 31 July 2026 Publisher: Springer Cham / Springer Nature Switzerland Edition: 1st edition Length: XIX + 312 pages ISBN: 978-3-032-04398-6 (Springer) Classical Machine Learning: A Practical Guide Using Python is designed as a bridge between the mathematical foundations of machine learning and their practical implementation in Python. Its central argument is that, despite the current attention given to generative AI and large neural networks, a solid understanding of classical machine-learning methods remains essential. The book therefore develops algorithms from relatively elementary techniques such as linear and logistic regression through Naïve Bayes, decision trees, random forests, ensemble methods, clustering and dimensionality reduction. Theory, mathematical intuition, and executable examples are deliberately combined rather than treating machine learning as simply calling library functions.  The book then makes a gradual transition from classical ML into modern neural-network methods. Later chapters introduce artificial neural networks, convolutional operations, convolutional neural networks (CNNs), recurrent neural networks (RNNs), transfer learning, and feature extraction. An unusual feature is its discussion of Cubixel representation, an emerging approach to image representation and processing. The progression therefore gives readers a useful conceptual path from traditional statistical learning to techniques underlying modern deep learning.  The practical side relies on NumPy, pandas, scikit-learn and TensorFlow, and the authors expect readers to have some familiarity with Python plus basic linear algebra and calculus. Consequently, it seems particularly suitable for students, researchers, data scientists, or programmers who know some Python but want to understand why machine-learning algorithms work rather than merely applying pre-built models. It is less a mathematically rigorous theoretical ML textbook than an applied introduction combining mathematics, explanation, and programming.  Main topics The book's 11 main chapters progress through: Data preprocessing and feature engineering Regression and linear models Classification — logistic regression through Naïve Bayes Decision trees and random forests Ensemble learning Clustering and dimensionality reduction Artificial neural networks Convolutional operations Convolutional neural networks Recurrent neural networks Transfer learning and features  Key takeaways Classical ML remains fundamental: understanding regression, trees, clustering, ensembles, and feature engineering makes modern AI considerably easier to understand. Strong theory–practice balance: mathematical ideas are paired with Python implementations rather than presented purely abstractly. Broad progression: the book starts with traditional statistical ML and eventually reaches CNNs, RNNs and transfer learning. Best suited to intermediate beginners: basic Python plus introductory calculus and linear algebra are recommended prerequisites.  Springer — Classical Machine Learning