Description:

Natural language processing is quietly everywhere — in the autocomplete on our phones, the chatbots that answer our questions, the translation apps that bridge languages, and the voice assistants we talk to every day. Yet learning how these systems actually work can feel overwhelming. This book makes natural language processing simple and accessible for everyone.

The book takes you on a clear journey from the very basics to modern AI systems. It starts with how raw, messy human text is cleaned and turned into numbers a machine can understand, then builds up step by step to today's powerful architectures—the transformer, BERT, GPT, and the large language models behind tools like ChatGPT.

What makes this book different? First, it explains ideas through intuition and examples before diving into the mathematics; the readers do not need an advanced background to begin. Second, it connects the ideas together, so the readers understand not just how each method works but why it was invented and when to use it. Third, it is deeply hands-on—every chapter includes Python labs using NLTK, spaCy, and Gensim, and a single running example is transformed step by step so the theory always stays concrete.

A special strength of this book is its attention to Indian languages and the realities of multilingual, code-mixed text. From Hinglish and Devanagari to transliteration and low-resource languages, it shows how NLP works for the languages we actually use, not just English.

Whether you are studying computer science, working on AI projects, or simply curious about how machines understand language, this book will give you both understanding and confidence. By the end, you will be ready to build real NLP systems and to make sense of the language technologies shaping our future.

Who should read this book? Primarily written for students beginning their NLP journey, the text will also be valuable for teachers designing courses and professionals entering the field.

KEY FEATURES

• Provides progressive learning to build concepts step by step showing how and why each idea evolved into the next.

• Explains every concept through intuition and worked examples before introducing the mathematics, making NLP approachable for students at all levels.

• Includes practical Python implementations in NLTK, spaCy, and Gensim in every chapter, with a single running example carried through the whole book so theory stays concrete.

• Focusses strongly on Indian languages covering multilingual, code-mixed reality of Indian text—Hinglish, Devanagari, transliteration, and low-resource NLP—alongside fairness, bias, and the responsible use of language models.

TARGET AUDIENCE

• B.Tech (Computer Science)

• B.Tech (AI & ML)

• B.Tech (Data Science)

• M.Sc. Computer Science