Books Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science Book by A. K. Md. Ehsanes Saleh, Resve A. Saleh, and Sohaib Majzoub

"Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data Science" by A. K. Md. Ehsanes Saleh, Resve A. Saleh, and Sohaib Majzoub is a comprehensive and accessible guide focused on addressing the often-overlooked challenge of outliers and anomalies in machine learning datasets.

The book emphasizes two primary approaches:

Using outlier-tolerant machine learning tools that can work effectively despite the presence of anomalous data.

Identifying and removing outliers before applying conventional machine learning methods.

Balancing theoretical foundations with practical application, it equips readers with skills to improve the accuracy, stability, and reliability of machine learning models by properly handling data irregularities. Practical Python code examples accompany the concepts to facilitate hands-on learning.

This volume is ideal for students and professionals in data science and machine learning who want to build robust, resilient models that maintain performance even when data contains noise, outliers, or anomalies. It highlights the importance of robust methods to avoid incorrect conclusions or decisions that could arise from ignoring such data issues.

Overall, the book serves as an essential resource for understanding and implementing methods to enhance machine learning systems' robustness in real-world, imperfect data environments