Blueprints for Text Analytics Using Python: Machine Learning-Based Solutions for Common Real World (NLP) Applications

Blueprints for Text Analytics Using Python is a comprehensive and application-focused book that helps data scientists, NLP practitioners, and Python developers create effective, scalable, and intelligent text analytics solutions using modern machine learning techniques.

Designed for those who want to move beyond the basics, this book offers real-world blueprints—fully functional templates—for solving common natural language processing (NLP) tasks. From building sentiment analyzers to deploying topic models, the content blends theoretical knowledge with practical implementation using Python’s robust ecosystem.

Key topics include:

• End-to-end text preprocessing pipelines

• Feature engineering for NLP tasks

• Supervised and unsupervised learning approaches for text data

• Named entity recognition, keyword extraction, and intent detection

• Developing intelligent chatbots and recommendation engines

• Leveraging libraries like spaCy, scikit-learn, Transformers, and NLTK

Each chapter acts as a blueprint—with real datasets, modular code snippets, and step-by-step instructions—enabling you to quickly apply solutions in domains like e-commerce, healthcare, social media, and customer support.

Whether you’re an aspiring NLP engineer or a data scientist aiming to deploy language-driven solutions, this book provides the skills and confidence to bring text intelligence to your applications.