Hands-On Natural Language Processing with Python

Turning text into useful models is the practical focus of Hands-On Natural Language Processing with Python. Rajesh Arumugam and Rajalingappaa Shanmugamani take readers from core NLP concepts to deep-learning approaches for tasks such as text classification, question answering, language translation, and speech processing.

The book pairs explanations with Python examples, introducing tools and architectures used to analyze language and build NLP applications. It is intended for developers and machine-learning practitioners; basic proficiency in Python and machine learning is recommended.

Start with the building blocks of language

The opening chapters establish key ideas in natural language processing and text analysis. Readers encounter text preprocessing and exploratory analysis alongside NLTK techniques such as tokenization, stemming, stop-word removal, part-of-speech tagging, and sentiment classification.

Connect neural networks to NLP tasks

From these foundations, the book introduces deep neural networks, TensorFlow, recurrent neural networks, and long short-term memory networks. It also explores word embeddings—the representation of words as vectors that can capture useful relationships in language data.

Explore practical applications

Later material applies these methods across a varied set of tasks, including text classification and generation, summarization, question answering, translation, speech recognition, and text-to-speech. The examples offer a route from NLP concepts toward implemented models, rather than treating language processing as theory alone.

For readers building with Python

This book may suit developers, data scientists, and machine-learning or NLP practitioners who want a code-oriented introduction to deep-learning applications for language. Familiarity with Python and machine-learning basics will help readers follow the technical material.

Published in 2018, the book presents the methods and tools of its publication period. For readers exploring that hands-on approach, it provides a structured path from text fundamentals to applied NLP models.