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Gain an overview of what spaCy offers for natural language processing

Learn details of spaCy's features and how to use them effectively

Work through practical recipes using spaCy

Description

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. 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.

Who is this book for?

This 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.

What you will learn

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

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brian chan Jul 23, 2024

Clear and concise presentation of NLP concepts and Spacy for beginner. Thoroughly enjoyed the working codes I can follow along.

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Kai Jul 20, 2021

As someone who is only brushing the surface of what NLP can do, this is a great book to get your proverbial toes wet.Bear in mind that you should have some working knowledge of how to code before you get into it. But once you have your foundations set, Duygu's book is a breeze to follow along and get a working model up and running in no time!I can't wait to homebrew with this newly found knowledge.

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Amazon Customer Oct 26, 2021

I came across Mastering spaCy when I was looking for references for my AI project which involves the use of transformers. I have extensive experience in many aspects of data science, however, NLP has always been an area I want to improve on. As I am reading this book, I can’t help but realize it has gradually become my favorite reference whenever I come across an AI project which more or less involves the implementation of NLP. I like this book for that it is not merely a textbook to the spaCy python library, but also an elaboration to the most commonly seen terms in NLP, such as POS tagging, tokenization, lemmatization, etc, with examples. Thus, reading this book allows me to learn about spaCy and linguistics simultaneously.To gain the most out of this book, I suggest that you prepare a vocabulary list and take notes on the terms appearing in this book that are intuitive to you. You should really take your time to taste the first 6 chapters of Mastering spaCy as it walks you through the general process of completing an NLP project, the concepts (NER and information extraction, etc) you might come across, and the functions in spaCy that apply these concepts.Additionally, I want to express my appreciation to the author, Miss Duygu Altinok, that I particularly enjoy the hands-on practice in chapter 8 and 9 as they are relevant to the project I am currently working on. I am able to borrow part of the codes from these two chapters to improve my own transformer. I strongly recommend this book to everyone who struggles with NLP. It influences me, and I believe it will influence you too!

Zhenya Oct 25, 2021

Mastering spaCy is a great book for those who want to work with spaCy. It is very thorough in its explanations of spaCy's features, including rule-based and machine learning tasks, and it is suitable for a beginner. Experts will likewise this book very useful as a reference in their daily work. It describes all the important features of spaCy, such as part-of-speech tagging, syntactic and semantic parsing, named entity recognition, word vectors, building and updating machine learning models, using deep learning and transformers, and a complete chatbot put together using spaCy. Overall, an excellent book for the NLP practitioner.

Emanuel Di Nardo Jul 13, 2021

To be honest, I'm liking this book a lot.It starts with a little introduction to the rise of NLP and of course SpaCy installation.It explains basic NLP concepts like tokenization, POS tagging, dependency parsing, Named Entity Recognition. Everything with a lot of python examples.It covers concepts like word embeddings, and more in general word numerical representation algorithms.The book covers also data annotation with tools like Brat and of course text classification with SpaCy and Keras.It has a section on Bert and Chatbots as well.Everything with a lot of code examples.I found this book very readable and complete.I recommend it.

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

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