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Natural Language Processing with spaCy

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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4 Hours15 Videos53 Exercises
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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.
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In the following Tracks

Machine Learning Scientist with Python

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Natural Language Processing in Python

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  1. 1

    Introduction to NLP and spaCy

    Free

    This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. You’ll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

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    Natural Language Processing (NLP) basics
    50 xp
    Doc container in spaCy
    100 xp
    NER use case
    50 xp
    Tokenization with spaCy
    100 xp
    spaCy basics
    50 xp
    Running a spaCy pipeline
    100 xp
    Lemmatization with spaCy
    100 xp
    Sentence segmentation with spaCy
    100 xp
    Linguistic features in spaCy
    50 xp
    POS tagging with spaCy
    100 xp
    NER with spaCy
    100 xp
    Text processing with spaCy
    100 xp
  2. 2

    spaCy Linguistic Annotations and Word Vectors

    Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

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  3. 3

    Data Analysis with spaCy

    Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

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  4. 4

    Customizing spaCy Models

    Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. You’ll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.

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In the following Tracks

Machine Learning Scientist with Python

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Natural Language Processing in Python

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Datasets

corona.json

Collaborators

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James Chapman
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Maham Khan
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Jasmin Ludolf
Azadeh Mobasher HeadshotAzadeh Mobasher

Principal Data Scientist

Azadeh is a principal data and applied scientist with more than 10 years of experience on applications of machine learning, natural language processing, optimization, and simulation in the tech, supply chain, and healthcare industries.
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