Natural Language Processing provides a comprehensive introduction to the principles, techniques, and applications of enabling computers to understand, interpret, and generate human language. The book combines fundamental concepts with modern advances in artificial intelligence and machine learning, offering readers both theoretical knowledge and practical insights into the rapidly evolving field of NLP.

Beginning with the fundamentals of text processing, linguistic analysis, and language representation, the book gradually explores advanced topics such as text classification, sentiment analysis, machine translation, information retrieval, named entity recognition, question answering, speech processing, and large language models (LLMs). It also introduces deep learning architectures, including Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Transformers, BERT, and GPT-based language models, with real-world applications and illustrative examples.

Designed for undergraduate and postgraduate students, researchers, educators, and industry professionals, this book bridges the gap between academic learning and practical implementation. Each chapter includes clear explanations, examples, case studies, and exercises to reinforce learning and support hands-on experimentation.

Whether you are beginning your journey in Natural Language Processing or seeking to deepen your understanding of modern language technologies, this book serves as a valuable resource for mastering NLP concepts and developing intelligent language-based applications.

Key Features

Comprehensive coverage from NLP fundamentals to advanced language models.
Detailed explanation of text preprocessing and linguistic concepts.
Practical examples and real-world case studies.
Coverage of machine learning and deep learning techniques for NLP.
Dedicated chapters on Transformers, BERT, GPT, and Large Language Models.
Exercises and review questions for self-assessment.
Suitable for students, researchers, educators, and AI practitioners.