Classical Machine Learning by Aburass, Sanad; Aljarah, Ibrahim;

Long description:

The field of Artificial Intelligence (AI) has rapidly transformed in recent years, with Machine Learning being now one of its most impactful and widely applied branches. From intelligent recommendation systems to self-driving cars, and from language translation to medical diagnosis, Machine Learning now touches nearly every aspect of modern life. Yet, for those beginning their journey into AI, the field can feel daunting—particularly with the increasing complexity of deep learning and generative models. In the midst of this fast-paced evolution, it is easy to overlook the foundational ideas that make these breakthroughs possible.

This book is written to bridge this gap and was born from the belief that a solid understanding of classical machine learning is not just helpful, but essential for truly grasping the advanced and modern models shaping today’s AI landscape. The authors’ goal is to explain classical models clearly and intuitively, while also providing hands-on Python implementations that bring these models to life and offering, as such, a balanced practical approach.

The authors cover a wide range of foundational topics, from linear regression and logistic regression to decision trees, ensemble methods, clustering, dimensionality reduction, neural networks, and convolutional operations. Emerging ideas like Cubixel representation in image processing are also presented, providing a forward-looking perspective on evolving practices. Each chapter builds on the last, combining theory, math, and code in a way that is accessible to students, researchers, and professionals alike.

The book assumes a working knowledge of Linear Algebra and Calculus, as many algorithms rely on these mathematical underpinnings. A solid foundation in Python is also recommended, since practical examples and implementations are written in Python with widely used libraries such as NumPy, pandas, scikit-learn, and TensorFlow. Whether you’re an aspiring machine learning engineer, a data scientist transitioning from another field, or an academic looking to refresh your knowledge, this book aims to be a practical companion on your learning journey.

Table of Contents:

Dedication.- Acknowledgment.- Preface. 1. Data Preprocessing & Feature Engineering.- 2. Regression & Linear Models.- 3. Classification: Logistic Regression to Naïve Bayes.- 4. Decision Trees and Random Forests.- 5. Ensemble Learning.- 6. Unsupervised Learning: Clustering & Dimensionality Reduction.- 7. Introduction to Artificial Neural Networks.- 8. Convolutional Operations.- 9. Convolutional Neural Networks.- 10. Recurrent Neural Networks.- 11. Transfer Learning Introduction to Features.- References.