Description

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

Category: Artificial Intelligence | Machine Learning | Data Science | Statistics | Predictive Modeling | Computer Science