Mastering spaCy

Mastering spaCy

eBook Details:

  • Paperback: 356 pages
  • Publisher: WOW! eBook (July 9, 2021)
  • Language: English
  • ISBN-10: 1800563353
  • ISBN-13: 978-1800563353

eBook Description:

Mastering spaCy: Build end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with ease

spaCy is an industrial-grade, efficient NLP Python library. It offers various pre-trained models and ready-to-use features. Mastering spaCy provides you with end-to-end coverage of spaCy’s features and real-world applications.

You’ll begin by installing spaCy and downloading models, before progressing to spaCy’s features and prototyping real-world NLP apps. Next, you’ll get familiar with visualizing with spaCy’s popular visualizer displaCy. The book also equips you with practical illustrations for pattern matching and helps you advance into the world of semantics with word vectors. Statistical information extraction methods are also explained in detail. Later, you’ll cover an interactive business case study that shows you how to combine all spaCy features for creating a real-world NLP pipeline. You’ll implement ML models such as sentiment analysis, intent recognition, and context resolution. The book further focuses on classification with popular frameworks such as TensorFlow’s Keras API together with spaCy. You’ll cover popular topics, including intent classification and sentiment analysis, and use them on popular datasets and interpret the classification results.

  • Install spaCy, get started easily, and write your first Python script
  • Understand core linguistic operations of spaCy
  • Discover how to combine rule-based components with spaCy statistical models
  • Become well-versed with named entity and keyword extraction
  • Build your own ML pipelines using spaCy
  • Apply all the knowledge you’ve gained to design a chatbot using spaCy

By the end of this Mastering spaCy book, you’ll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.

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