Natural Language Processing (NLP) is a practical and comprehensive guide designed for students, beginners, developers, and AI enthusiasts who want to understand how computers process, analyze, and generate human language.

The book begins with the fundamentals of NLP and gradually progresses to advanced concepts such as text preprocessing, feature extraction, machine learning, deep learning, word embeddings, sequence models, attention mechanisms, Transformers, and Large Language Models (LLMs).

With a hands-on and project-oriented approach, readers will learn how to process text data, build NLP models, perform sentiment analysis and text classification, develop conversational applications, and apply modern NLP techniques to real-world problems.

What You Will Learn
Introduction to Natural Language Processing
Applications of NLP
Text Data and Language Processing
NLP Pipeline
Text Preprocessing
Tokenization
Stop Word Removal
Stemming and Lemmatization
Part-of-Speech (POS) Tagging
Named Entity Recognition (NER)
Regular Expressions
Text Cleaning and Normalization
Bag of Words
TF-IDF
N-Grams
Word Embeddings
Word2Vec and GloVe
Text Classification
Sentiment Analysis
Spam Detection
Machine Learning for NLP
Naive Bayes for Text Classification
Logistic Regression for NLP
Deep Learning for NLP
Artificial Neural Networks
Recurrent Neural Networks (RNN)
LSTM and GRU
Sequence-to-Sequence Models
Attention Mechanism
Transformer Architecture
BERT and GPT Concepts
Transfer Learning for NLP
Large Language Models (LLMs)
Prompt Engineering for NLP Applications
Retrieval-Augmented Generation (RAG)
NLP with Python
NLTK and spaCy
Scikit-learn for NLP
Hugging Face Transformers
Text Summarization
Machine Translation
Question Answering
Chatbots and Conversational AI
NLP Model Evaluation
Real-World NLP Projects