Classical Machine Learning: A Practical Guide Using Python

Classical Machine Learning: A Practical Guide Using Python provides a clear and practical foundation in one of artificial intelligence’s most important branches.

As machine learning grows increasingly complex through deep learning and generative models, the book returns to the foundational ideas that make modern AI possible. It explains classical models intuitively and pairs the theory with hands-on Python implementations.

What the book covers

• Linear regression and logistic regression

• Decision trees and ensemble methods

• Clustering and dimensionality reduction

• Neural networks and convolutional operations

• Emerging ideas such as Cubixel representation in image processing

• Theory, mathematics, code, and practical implementations

The chapters build progressively, combining conceptual explanations with mathematical foundations and code. Practical examples use widely adopted Python libraries including NumPy, pandas, scikit-learn, and TensorFlow.

Recommended background

Readers should have working knowledge of Linear Algebra and Calculus because many algorithms rely on these mathematical foundations. A solid Python foundation is also recommended.

This book is designed for aspiring machine learning engineers, data scientists moving from another field, students, researchers, professionals, and academics who want to strengthen or refresh their classical machine learning knowledge.

Product details

Author: Ibrahim Aljarah

Publisher: Springer

Edition: 2026

Release date: August 21, 2026

Language: English

Length: 324 pages

Formats: True PDF and True EPUB

ISBN: 103032043980