Linear and Nonlinear Regression in Artificial Intelligenc VOL-2 Mathematical Foundations, Regularization Techniques

Free Download Linear and Nonlinear Regression in Artificial Intelligenc VOL-2: Mathematical Foundations, Regularization Techniques & Predictive Modeling (AI AND MATH NEW)
English | December 1, 2025 | ASIN: B0G4LNYPLK | 304 pages | Epub | 278.76 KB
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: 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 sentiments in text, or optimizing machine performance, regression is everywhere. This book, Linear and Nonlinear Regression in Artificial Intelligence , written by Anshuman Mishra , is crafted to be one of the most comprehensive and practical books for students, researchers, working professionals, and academicians in computer science, data analytics, mathematics, and AI/ML engineering. The book combines mathematical depth , practical implementation , and real-world use cases -making it equally suitable for classroom instruction, academic reference, research exploration, and professional applications. 🎯 Purpose of the Book The purpose of this book is to guide the reader from the foundational principles of regression to the advanced, state-of-the-art regression algorithms used in modern AI systems. While most books either focus only on statistics, or only on machine learning, this book combines: 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 Interview and research-driven insights This combination makes the book a complete, end-to-end reference that caters to university-level learners as well as professionals working in industries like finance, healthcare, IT, manufacturing, e-commerce, analytics, or scientific research. 📌 Why This Book Stands Out Regression is one of the first topics introduced in statistics and machine learning, yet in the AI era it has evolved into highly sophisticated modeling techniques. Many textbooks treat regression superficially, but in real-world AI systems, regression methods must address complexities such as: High-dimensional data Noise and missing values Complex nonlinear relationships Overfitting and underfitting Bias-variance tradeoff Regularized learning Kernel methods Deep learning models Probabilistic reasoning Uncertainty estimation Big data scalability Interpretability of models This book captures all these essential nuances, making it uniquely rich and powerful. It ensures that the reader not only understands the mathematics but can also implement, interpret, and deploy regression systems in real-world AI applications. 📚 What This Book Covers (Overview) The book begins with the core foundations of regression and its importance in AI. It builds step-by-step: 1. Mathematical Foundations Before diving into regression techniques, readers will gain clarity on 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