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Natural Language Processing with spaCy

Intermediate
Updated 12/2024
Master the core operations of spaCy and train models for natural language processing. Extract information from unstructured data and match patterns.
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PythonMachine Learning4 hours15 videos53 exercises4,450 XP4,222Statement of Accomplishment

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Course Description

Meet spaCy, an Industry-Standard for NLP

In this course, you will learn how to use spaCy, a fast-growing industry-standard library, to perform various natural language processing tasks such as tokenization, sentence segmentation, parsing, and named entity recognition. spaCy can provide powerful, easy-to-use, and production-ready features across a wide range of natural language processing tasks.

Learn the Core Operations of spaCy

You will start by learning the core operations of spaCy and how to use them to parse text and extract information from unstructured data. Then, you will work with spaCy’s classes, such as Doc, Span, and Token, and learn how to use different spaCy components for calculating word vectors and predicting semantic similarity.

Train spaCy Models and Learn About Pattern Matching

You will practice writing simple and complex matching patterns to extract given terms and phrases using EntityRuler, Matcher, and PhraseMatcher from unstructured data. You will also learn how to create custom pipeline components and create training/evaluation data. From there, you will dive into training spaCy models and how to use them for inference. Throughout the course, you will work on real-world examples and solidify your understanding of using spaCy in your own NLP projects.

Prerequisites

Supervised Learning with scikit-learnPython Toolbox
1

Introduction to NLP and spaCy

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2

spaCy Linguistic Annotations and Word Vectors

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3

Data Analysis with spaCy

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4

Customizing spaCy Models

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Natural Language Processing with spaCy
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