Classical Machine Learning
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.
More details
Persons
Sanad Aburass is an Assistant Professor of Computer Science at Luther College in Iowa, USA. He holds a Ph.D. in Computer Science from the University of Jordan, specializing in Machine Learning and Computer Vision. Dr. Aburass teaches a range of courses in machine learning, algorithms, data science, web programming and object-oriented programming, emphasizing real-world application and student-centered learning. He is an active researcher with numerous publications in top-tier journals and conferences, and he serves as a guest editor for a Research Topic at Frontiers in Medicine . He also holds a registered patent in Germany for a social media-based targeted marketing framework. In addition to his research in machine learning and computer vision, Dr. Aburass has presented widely on philosophical topics at academic conferences and has published several works in this field. He regularly contributes articles to the Jordanian newspaper Addustour, addressing themes in philosophy, psychology, society, and technology. His book United Martians: A Trip to the Future explores philosophical, psychological, and sociological ideas aimed at fostering a more harmonious and united society.
Ibrahim Aljarah is currently a Professor of Artificial Intelligence at the University of Jordan, Amman, Jordan, as well as a Chief AI Officer and executive consultant at Arrowad Group and Kaizen Consulting, where he leads initiatives and strategies related to artificial intelligence, driving innovation and technological advancement within the organization and its clients. He is a distinguished researcher, recognized globally for his expertise in artificial intelligence, data mining, and big data. He is also a Highly Cited Researcher (Clarivate Analytics) and a Senior Member of IEEE (SMIEEE), with numerous publications ranked in the top 1% by citations according to Web of Science. He earned his Ph.D. in Computer Science from North Dakota State University in 2014 and served as Head of the AI Department at the University of Jordan in 2022. From 2019 to 2025, he has been among the university's top publishing researchers, with over 140 high-impact publications, 3 books, more than 20,200 citations, and an h-index of 60. Prof. Aljarah has received several prestigious awards, including the UJ Distinguished Researcher Award (2022) and the Ali Mango Award (2020). He ranks among the top 2% of scientists worldwide and among the top 10 in Jordan in AI and image processing, according to Stanford University (2020-2025). He has presented at major international conferences and contributed to key projects in the U.S., such as the Vehicle Class Detection System and PAVVET. His research interests span machine learning, swarm intelligence, evolutionary computation, and big data technologies.
Content
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.