Course
Natural Language Processing with spaCy
IntermediateSkill Level
Updated 07/2025
PythonMachine Learning4 hr15 videos53 Exercises4,450 XP8,000Statement of Accomplishment
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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 Toolbox1
Introduction to NLP and spaCy
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.
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.
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.
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.
Natural Language Processing with spaCy
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FAQs
What makes spaCy different from other NLP libraries like NLTK?
spaCy is designed for production use with fast processing speeds and pre-trained models. This course focuses on spaCy's industrial-strength tools for tokenization, parsing, and named entity recognition.
Will I learn to train custom NLP models with spaCy?
Yes. Chapter 4 covers training existing spaCy models further and training models from scratch, then evaluating their performance at inference time on real-world use cases.
What rule-based extraction techniques does the course teach?
Chapter 3 covers EntityRuler, Matcher, PhraseMatcher, and RegEx for pattern-based information extraction. You also learn to add custom components to the spaCy pipeline.
How many prerequisites does this advanced course require?
Seven prerequisites are listed, including Intermediate Python, Data Manipulation with pandas, Supervised Learning with scikit-learn, and Introduction to Statistics in Python.
What practical NLP tasks will I be able to perform after this course?
You will be able to tokenize text, perform POS tagging, extract named entities, compute word vectors, measure semantic similarity, build custom pipelines, and train spaCy models.
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