Linear and Nonlinear Regression in Artificial Intelligenc VOL-2: Mathematical Foundations, Regularization Techniques & Predictive Modeling

Artificial Intelligence has rapidly transitioned from a specialized research domain to the backbone of multiple industries, transforming the way we interpret data, make decisions, and build intelligent systems. At the core of many AI driven applications, whether in finance, healthcare diagnostics, business analytics, scientific modeling, robotics, or autonomous systems, lies one powerful set of mathematical tools known as linear and nonlinear regression models.Regression is not merely a statistical method. It is one of the most fundamental predictive modeling techniques that enables machines to learn patterns, identify relationships, understand trends, and make informed predictions. Whether it is forecasting stock prices, predicting disease risks, modeling customer behavior, estimating energy consumption, detecting fraud, recognizing speech patterns, analyzing sentiment in text, or optimizing machine performance, regression plays a central role.This book, Linear and Nonlinear Regression in Artificial Intelligence, written by Anshuman Mishra, is designed to be one of the most comprehensive and practical resources for students, researchers, working professionals, and academicians in computer science, data analytics, mathematics, and AI and machine learning engineering. The book integrates mathematical depth, practical implementation, and real world use cases, making it suitable for classroom instruction, academic reference, research work, and professional applications.Purpose of the BookThe purpose of this book is to guide the reader from the foundational principles of regression to advanced regression algorithms used in modern artificial intelligence systems. While many books focus only on statistics or only on machine learning, this book presents an integrated approach that combines multiple perspectives. It emphasizes mathematical clarity, statistical fundamentals, machine learning theory, optimization techniques, regularization and generalization, modern nonlinear models, advanced AI methodologies, Python based implementations, real world case studies, and interview and research oriented insights.This approach makes the book a complete end to end reference for university level learners as well as professionals working in industries such as finance, healthcare, information technology, manufacturing, e commerce, analytics, and scientific research.Why This Book Stands OutRegression is one of the earliest topics introduced in statistics and machine learning, yet in the artificial intelligence era it has evolved into highly sophisticated modeling techniques. Many textbooks address regression only at a surface level, but real world AI systems require regression methods that can handle high dimensional data, noise and missing values, complex nonlinear relationships, overfitting and underfitting, bias variance tradeoff, regularized learning, kernel based methods, deep learning models, probabilistic reasoning, uncertainty estimation, scalability for large datasets, and model interpretability.This book addresses all these challenges in a structured and practical manner. It ensures that readers not only understand the underlying mathematics but are also able to implement, interpret, and deploy regression based systems in real world AI applications.What This Book CoversThe book begins with the core foundations of regression and its role in artificial intelligence. It then progresses step by step, starting with mathematical foundations. Before exploring regression techniques, readers gain a clear understanding of essential linear algebra, vector calculus, optimization principles, probability theory, and statistical learning concepts. These mathematical tools are explained intuitively and applied directly to regression models. Read more