Artificial Intelligence increasingly relies on sophisticated regression techniques capable of modeling nonlinear relationships, handling uncertainty, scaling to massive datasets, and providing interpretable predictions. As machine learning systems become more complex, traditional regression approaches are complemented by probabilistic models, kernel methods, deep learning architectures, and explainable AI techniques.

Linear and Nonlinear Regression in Artificial Intelligence: Mathematical Foundations, Regularization Techniques & Predictive Modeling (Vol-II) continues the journey begun in Volume I by focusing on advanced regression methodologies used in modern AI research and industrial applications.

This volume introduces Support Vector Regression (SVR), Neural Network Regression, Bayesian Regression, Gaussian Process Regression (GPR), scalable regression algorithms for big data, explainable AI, and practical machine learning implementation using Python. It also includes complete end-to-end regression projects, interview preparation, and discussions of emerging research directions.

Combining mathematical derivations, optimization principles, Python programming, and real-world case studies, this book provides readers with both theoretical understanding and practical expertise required to build intelligent predictive systems.

Whether you are a student, educator, researcher, or AI professional, this volume serves as an advanced guide to regression techniques powering today’s intelligent applications.

What You Will Learn

✔ Support Vector Regression (SVR)

✔ Linear SVR

✔ Kernel SVR

✔ Radial Basis Function (RBF) Kernel

✔ Polynomial Kernel

✔ Sigmoid Kernel

✔ Epsilon-Insensitive Loss Function

✔ Neural Network Regression

✔ Perceptron Models

✔ Multi-Layer Perceptron (MLP)

✔ Deep Learning Regression

✔ Activation Functions

✔ Backpropagation Algorithm

✔ Dropout Regularization

✔ Bayesian Regression

✔ Prior and Posterior Distributions

✔ Bayesian Linear Regression

✔ MAP Estimation

✔ Maximum Likelihood Estimation (MLE)

✔ Bayesian Ridge Regression

✔ Bayesian Lasso Regression

✔ Hierarchical Bayesian Models

✔ Gaussian Process Regression (GPR)

✔ Kernel Functions

✔ Covariance Functions

✔ Uncertainty Estimation

✔ Robotics Optimization

✔ Regression with Large Datasets

✔ Stochastic Gradient Descent (SGD)

✔ Mini-Batch Learning

✔ Online Learning

✔ Distributed Machine Learning

✔ Apache Spark ML

✔ Explainable AI (XAI)

✔ SHAP Values

✔ LIME

✔ Feature Importance

✔ Interpretable Machine Learning

✔ Ethical AI

✔ Regression Applications

✔ Computer Vision

✔ Natural Language Processing (NLP)

✔ Healthcare AI

✔ Financial Forecasting

✔ Time Series Forecasting

✔ Climate Modeling

✔ Scientific Computing

✔ Python Implementation

✔ NumPy

✔ Pandas

✔ Matplotlib

✔ Scikit-learn

✔ TensorFlow

✔ PyTorch

✔ Hyperparameter Optimization

✔ Optuna

✔ GridSearchCV

✔ Random Search

✔ End-to-End AI Projects

✔ Technical Interview Preparation

✔ Emerging AI Research Trends

Key Features

• Comprehensive coverage of advanced regression techniques

• Mathematical explanations with practical intuition

• Complete treatment of Support Vector Regression

• Deep Learning regression models explained from fundamentals

• Bayesian and probabilistic regression methods

• Gaussian Process Regression for uncertainty-aware AI

• Big Data regression using scalable learning techniques

• Explainable AI with SHAP and LIME

• Industry-oriented Python implementation

• Real-world predictive modeling case studies

• Interview-focused questions and research insights

• Suitable for academic, research, and industrial applications

Table of Contents Highlights

Unit IV – Advanced Nonlinear Regression in AI

Support Vector Regression
Linear SVR
Kernel SVR
RBF Kernel
Polynomial Kernel
Sigmoid Kernel
Neural Network Regression
Multi-Layer Perceptron
Deep Learning Regression
Backpropagation
Dropout

Unit V – Probabilistic & Bayesian Regression

Bayesian Regression
Prior and Posterior
MAP vs MLE
Bayesian Ridge
Bayesian Lasso
Hierarchical Bayesian Models
Gaussian Process Regression
Kernel Functions
Covariance Matrices
Prediction with Uncertainty

Unit VI – Regression in AI Systems

Big Data Regression
Stochastic Gradient Descent
Mini-Batch Optimization
Online Learning
Spark ML
Explainable AI
SHAP
LIME
Ethical AI
Regression Applications
Computer Vision
NLP
Healthcare
Finance
Time Series
Climate Science

Unit VII – Practical Python Implementation

NumPy
Pandas
Matplotlib
Scikit-learn
TensorFlow
PyTorch
Hyperparameter Optimization
Optuna
GridSearchCV
Random Search
Housing Price Prediction
Customer Churn Prediction
Medical Risk Prediction
Energy Forecasting
AI Interview Questions
Research Trends

Who Should Read This Book?

This book is ideal for:

BCA Students
MCA Students
B.Tech (Computer Science & IT)
M.Tech Students
M.Sc. Computer Science Students
Artificial Intelligence Students
Machine Learning Students
Data Science Students
Statistics Students
AI Researchers
PhD Scholars
Computer Science Faculty
Data Scientists
Machine Learning Engineers
Software Engineers
AI Professionals

Why This Book?

Modern predictive systems demand more than classical regression models. Applications in healthcare, finance, robotics, autonomous systems, and scientific computing require algorithms capable of handling uncertainty, nonlinear relationships, high-dimensional data, and explainable predictions.

Linear and Nonlinear Regression in Artificial Intelligence (Vol-II) bridges mathematical theory with practical AI implementation by presenting advanced regression techniques alongside real-world applications and Python programming.

Unlike conventional textbooks that focus only on algorithms, this volume integrates optimization, probabilistic reasoning, scalable machine learning, explainable AI, and complete project development into one structured learning resource.

Readers gain both conceptual understanding and implementation skills necessary for academic research, industrial AI development, competitive examinations, technical interviews, and professional machine learning careers.

Suitable For

BCA
MCA
B.Tech (Computer Science & IT)
M.Tech
M.Sc. Computer Science
Artificial Intelligence Programs
Machine Learning Courses
Data Science Programs
Statistics Courses
Predictive Analytics Programs
Research Projects
Competitive Examinations (GATE, UGC-NET, PhD Entrance)
Academic Libraries
Professional AI Training

Book Details

Title: Linear and Nonlinear Regression in Artificial Intelligence

Subtitle: Mathematical Foundations, Regularization Techniques & Predictive Modeling (Vol-II)

Author: Anshuman Mishra

Language: English

Subject: Artificial Intelligence, Machine Learning, Regression Analysis, Predictive Analytics, Data Science

Level: Intermediate to Advanced

Applied Artificial Intelligence.

Linear and Nonlinear Regression in Artificial Intelligenc VOL-2

Optimization Techniques in Artificial Intelligence: Foundations, Mathematical Models, Algorithms, and Real-World Applications (Vol-1)