Artificial Language is a practical and comprehensive guide to understanding how computers process, analyze, understand, and generate human language. The book introduces the fundamental concepts of language processing and gradually progresses to modern Artificial Intelligence and Natural Language Processing techniques.

Readers will learn how text and language are represented computationally, how language data is processed, and how machine learning and deep learning techniques can be used to build intelligent language-based applications. The book covers text preprocessing, linguistic analysis, feature extraction, machine learning, neural networks, sequence models, attention mechanisms, Transformers, Large Language Models, and Generative AI.

With practical examples and real-world applications, this book provides a structured foundation for students, developers, and AI enthusiasts who want to understand computational language technologies and build intelligent language-processing systems.

What You Will Learn
Introduction to Artificial Language and Computational Language
Artificial Intelligence and Language Processing
Natural Language Processing Fundamentals
Computational Linguistics
Human Language and Machine Understanding
Text Representation and Processing
Text Cleaning and Normalization
Tokenization
Stop Words Removal
Stemming and Lemmatization
Part-of-Speech Tagging
Named Entity Recognition
Regular Expressions for Text Processing
Bag of Words and N-Grams
TF-IDF and Feature Extraction
Word Embeddings
Word2Vec and GloVe
Text Classification
Sentiment Analysis
Spam Detection
Machine Learning for Language Processing
Naive Bayes and Logistic Regression
Neural Networks for NLP
RNN, LSTM, and GRU
Sequence-to-Sequence Models
Attention Mechanisms
Transformers
BERT and GPT
Large Language Models
Generative AI and Language Models
Prompt Engineering
Retrieval-Augmented Generation (RAG)
AI Chatbots and Conversational Systems
Text Summarization
Machine Translation
Question Answering Systems
Text Generation
Speech and Language Applications
NLTK and spaCy
Scikit-learn for NLP
Hugging Face Transformers
Language Model APIs
Model Evaluation
Handling Bias and Hallucination
Responsible AI and Language Safety
Real-World AI Language Projects