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Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem
Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem
What makes this book stand out is that it’s the first to cover spaCy in depth.
Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem
Item #: 36943506

Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem

Item #: 36943506

ZAR 1330

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What makes this book stand out is that it’s the first to cover spaCy in depth.
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What Stands Out

Comprehensive Guide
Offers an in-depth exploration of spaCy for NLP, catering to beginners and experts alike, ensuring mastery through practical applications and clear explanations.
Python Ecosystem
Integrates seamlessly with Python tools and libraries, providing users with a powerful framework to build, deploy, and enhance NLP applications efficiently.
Real-World Applications
Focuses on implementing real-world NLP solutions, helping users to solve actual problems and gain hands-on experience with useful, actionable knowledge.

Product Details

Shop Mastering spaCy: An end-to-end practical guide to implementing NLP applications using the Python ecosystem online at a best price in South Africa. 1800563353
  • Build end-to-end industrial-strength NLP models using advanced morphological and syntactic features in spaCy to create real-world applications with easeKey FeaturesGain an overview of what spaCy offers for natural language processingLearn details of spaCy's features and how to use them effectivelyWork through practical recipes using spaCyBook DescriptionspaCy 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.By the end of this book, you'll be able to confidently use spaCy, including its linguistic features, word vectors, and classifiers, to create your own NLP apps.What you will learnInstall spaCy, get started easily, and write your first Python scriptUnderstand core linguistic operations of spaCyDiscover how to combine rule-based components with spaCy statistical modelsBecome well-versed with named entity and keyword extractionBuild your own ML pipelines using spaCyApply all the knowledge you've gained to design a chatbot using spaCyWho this book is forThis book is for data scientists and machine learners who want to excel in NLP as well as NLP developers who want to master spaCy and build applications with it. Language and speech professionals who want to get hands-on with Python and spaCy and software developers who want to quickly prototype applications with spaCy will also find this book helpful. Beginner-level knowledge of the Python programming language is required to get the most out of this book. A beginner-level understanding of linguistics such as parsing, POS tags, and semantic similarity will also be useful.Table of ContentsGetting Started with spaCyCore Operations with spaCyLinguistic FeaturesRule-Based MatchingWorking with Word Vectors and Semantic SimilarityPutting Everything Together: Semantic Parsing with spaCyCustomizing spaCy ModelsText Classification with spaCyspaCy and TransformersPutting Everything Together: Designing Your Chatbot with spaCy
Publisher Packt Publishing
Publication date July 9, 2021
Language English
Print length 356 pages
ISBN-10 1800563353
ISBN-13 978-1800563353
Item Weight 1.35 pounds (610 grams)
Dimensions 7.5 x 0.81 x 9.25 inches (19.1 x 2.1 x 23.5 cm)

Who Should Buy?

Suitable For
  • NLP Beginners

    Ideal for those new to Natural Language Processing, providing step-by-step guidance on using spaCy effectively.

  • Python Developers

    Perfect for Python developers looking to enhance their skills in NLP and integrate these applications into existing projects.

  • Data Scientists

    Beneficial for data scientists wanting to incorporate NLP into their analysis and utilize text data for insights.

Not Suitable For
  • Advanced Researchers

    Not suitable for seasoned NLP researchers seeking in-depth theoretical concepts beyond practical application-focused content.

  • Non-Programmers

    Inappropriate for individuals without programming experience, as it heavily relies on Python coding to demonstrate concepts.

  • Occasional Users

    Less beneficial for those only needing quick solutions in NLP without a commitment to deeper learning or practice.

Product Description

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Intelligence & Semantics Editorial Review

Mastering spaCy is a comprehensive guide that covers the end-to-end process of implementing natural language processing applications using Python. The book provides solid and structured content, making it easy for beginners to understand the basics of spaCy. The author shares a wealth of knowledge in the book, but readers may face challenges due to poor editing. Many lines of code are incorrect or missing, and readers will have to spend additional time researching to obtain the correct codes. Despite these challenges, the book provides a good learning experience for readers who are patient enough to troubleshoot the errors.

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Pros

  • Solid and well-structured content
  • Comprehensive guide to implementing natural language processing applications

Cons

  • Poor editing, with frequent typos, errors in code, and lines that are missing

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