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Applied NLP with Python: Text Cleaning, Embeddings, and Real-World Use
Paperback
DatabasesGeneral ComputersProgramming
Publisher: Independently Published
Published: Feb 9 2026
Pages: 236
Weight: 0.71
Height: 0.50 Width: 6.00 Depth: 9.00
Language: English
Natural Language Processing (NLP) is powering search engines, chatbots, recommendation systems, sentiment analysis, and large language models-but most books stop at theory.
Applied NLP with Python is a practical, hands-on guide designed to help you move from messy, unstructured text to production-ready NLP solutions using Python.
Whether you're a data scientist, machine learning engineer, software developer, or researcher, this book shows you how NLP actually works in the real world-from cleaning noisy text to building embeddings and deploying meaningful applications.
What you'll learn:Clean, normalize, and preprocess real-world text data
Handle tokenization, stemming, lemmatization, and stopwords effectively
Build and evaluate text features using traditional and modern approaches
Understand and implement word, sentence, and document embeddings
Work with TF-IDF, Word2Vec, GloVe, FastText, and transformer-based embeddings
Apply NLP techniques to classification, clustering, similarity search, and sentiment analysis
Design scalable NLP pipelines using Python libraries such as NLTK, spaCy, scikit-learn, and transformers
Translate theory into business-ready NLP use cases
- Focuses on applied NLP, not abstract math
- Uses clean, readable Python code throughout
- Covers both classic techniques and modern embedding methods
- Emphasizes real-world datasets and problems
- Ideal bridge between beginner tutorials and advanced NLP systems
Data scientists and analysts working with text data
Python developers entering NLP or AI
Machine learning practitioners seeking practical NLP skills
Students and professionals preparing for real-world NLP projects
No unnecessary theory. No black-box explanations.
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