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{ "item_title" : "Applied NLP with Python", "item_author" : [" Amara Hawthorn "], "item_description" : "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 dataHandle tokenization, stemming, lemmatization, and stopwords effectivelyBuild and evaluate text features using traditional and modern approachesUnderstand and implement word, sentence, and document embeddingsWork with TF-IDF, Word2Vec, GloVe, FastText, and transformer-based embeddingsApply NLP techniques to classification, clustering, similarity search, and sentiment analysisDesign scalable NLP pipelines using Python libraries such as NLTK, spaCy, scikit-learn, and transformersTranslate theory into business-ready NLP use casesWhy this book stands out: Focuses on applied NLP, not abstract mathUses clean, readable Python code throughoutCovers both classic techniques and modern embedding methodsEmphasizes real-world datasets and problemsIdeal bridge between beginner tutorials and advanced NLP systemsWho this book is for: Data scientists and analysts working with text dataPython developers entering NLP or AIMachine learning practitioners seeking practical NLP skillsStudents and professionals preparing for real-world NLP projectsNo unnecessary theory. No black-box explanations.", "item_img_path" : "https://covers4.booksamillion.com/covers/bam/9/79/824/547/9798245472331_b.jpg", "price_data" : { "retail_price" : "18.99", "online_price" : "18.99", "our_price" : "18.99", "club_price" : "18.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Applied NLP with Python|Amara Hawthorn

Applied NLP with Python : Text Cleaning, Embeddings, and Real-World Use

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Overview

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

Why this book stands out:
  • 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
Who this book is for:
  • 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.

This item is Non-Returnable

Details

  • ISBN-13: 9798245472331
  • ISBN-10: 9798245472331
  • Publisher: Independently Published
  • Publish Date: February 2026
  • Dimensions: 9 x 6 x 0.5 inches
  • Shipping Weight: 0.71 pounds
  • Page Count: 236

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