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Linear and Nonlinear Regression in Artificial Intelligence

Mathematical Foundations, Regularization Techniques & Predictive Modeling (Vol-I)

Artificial Intelligence is fundamentally about learning patterns from data and transforming those patterns into actionable predictions. Among the many techniques that power modern AI systems, regression remains one of the most influential, versatile, and widely used methodologies.

From predicting housing prices and financial risks to estimating medical outcomes, forecasting energy demand, understanding customer behavior, and optimizing intelligent systems, regression models form the backbone of predictive analytics and machine learning.

Linear and Nonlinear Regression in Artificial Intelligence: Mathematical Foundations, Regularization Techniques & Predictive Modeling (Vol-I) is a comprehensive guide that takes readers from the mathematical foundations of regression to advanced machine learning models used in contemporary AI applications.

Unlike traditional statistics textbooks that focus primarily on theory, or machine learning books that emphasize implementation without sufficient mathematical depth, this volume bridges both worlds. It combines rigorous mathematical explanations with practical machine learning techniques, algorithmic insights, and real-world AI applications.

Readers will explore:

• Mathematical Foundations of Regression Modeling

• Linear Algebra, Optimization, and Statistical Learning

• Probability Theory and Bayesian Concepts

• Simple and Multiple Linear Regression

• Least Squares Estimation and Gradient Descent

• Regularization Techniques (Ridge, Lasso, Elastic Net)

• Polynomial and Nonlinear Regression Models

• Logistic Regression and Classification

• Generalized Linear Models (GLMs)

• Decision Tree Regression and Ensemble Methods

• Support Vector Regression (SVR)

• Neural Network-Based Regression Models

• Bayesian Regression and Gaussian Processes

• Large-Scale Regression for Big Data Systems

• Explainable AI and Regression Interpretability

• End-to-End Regression Projects using Python

This book emphasizes both theoretical understanding and practical implementation. Each concept is supported by mathematical derivations, intuitive explanations, worked examples, case studies, and industry-oriented applications.

Whether the goal is academic learning, research exploration, interview preparation, or professional AI development, this book provides a complete framework for mastering regression-based predictive modeling.

Who Should Read This Book?

• BCA, MCA, B.Tech, M.Tech and Computer Science Students

• Artificial Intelligence and Machine Learning Learners

• Data Scientists and Analytics Professionals

• Researchers and PhD Scholars

• Software Engineers and AI Developers

• Business Intelligence and Predictive Analytics Professionals

• UGC-NET, GATE and Technical Interview Aspirants

• Anyone seeking a strong foundation in predictive modeling

What Makes This Book Unique?

✔ Combines mathematics, statistics, machine learning, and AI in one unified framework

✔ Covers both linear and advanced nonlinear regression methods

✔ Includes modern ensemble, kernel, neural network, and Bayesian approaches

✔ Emphasizes model explainability and ethical AI practices

✔ Provides practical Python implementations and industry case studies

✔ Suitable for both academic and professional learning

This volume serves as both a university-level textbook and a professional reference guide for understanding how regression powers intelligent decision-making systems across modern Artificial Intelligence.

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Kick off your book project in 3 hours! Live workshop on Zoom. You’ll leave with a real book project, progress on your first chapter, and a clear plan to keep going. Saturday, June 6, 2026. Learn more…

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