Natural Language Processing (NLP) (A Complete Guide to NLP, Text Preprocessing, Machine Learning, Deep Learning, Transformers, LLMs, and Real-World Applications) by Aravind choudhary

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