Interactive Course

Advanced NLP with spaCy

Learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

  • 5 hours
  • 15 Videos
  • 55 Exercises
  • 3,905 Participants
  • 4,450 XP

Loved by learners at thousands of top companies:

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Course Description

If you're working with a lot of text, you'll eventually want to know more about it. For example, what's it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other? In this course, you'll learn how to use spaCy, a fast-growing industry standard library for NLP in Python, to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

  1. 1

    Finding words, phrases, names and concepts

    Free

    This chapter will introduce you to the basics of text processing with spaCy. You'll learn about the data structures, how to work with statistical models, and how to use them to predict linguistic features in your text.

  2. Processing Pipelines

    This chapter will show you to everything you need to know about spaCy's processing pipeline. You'll learn what goes on under the hood when you process a text, how to write your own components and add them to the pipeline, and how to use custom attributes to add your own meta data to the documents, spans and tokens.

  3. Large-scale data analysis with spaCy

    In this chapter, you'll use your new skills to extract specific information from large volumes of text. You'll learn how to make the most of spaCy's data structures, and how to effectively combine statistical and rule-based approaches for text analysis.

  4. Training a neural network model

    In this chapter, you'll learn how to update spaCy's statistical models to customize them for your use case – for example, to predict a new entity type in online comments. You'll write your own training loop from scratch, and understand the basics of how training works, along with tips and tricks that can make your custom NLP projects more successful.

  1. 1

    Finding words, phrases, names and concepts

    Free

    This chapter will introduce you to the basics of text processing with spaCy. You'll learn about the data structures, how to work with statistical models, and how to use them to predict linguistic features in your text.

  2. Large-scale data analysis with spaCy

    In this chapter, you'll use your new skills to extract specific information from large volumes of text. You'll learn how to make the most of spaCy's data structures, and how to effectively combine statistical and rule-based approaches for text analysis.

  3. Processing Pipelines

    This chapter will show you to everything you need to know about spaCy's processing pipeline. You'll learn what goes on under the hood when you process a text, how to write your own components and add them to the pipeline, and how to use custom attributes to add your own meta data to the documents, spans and tokens.

  4. Training a neural network model

    In this chapter, you'll learn how to update spaCy's statistical models to customize them for your use case – for example, to predict a new entity type in online comments. You'll write your own training loop from scratch, and understand the basics of how training works, along with tips and tricks that can make your custom NLP projects more successful.

What do other learners have to say?

Devon

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Devon Edwards Joseph

Lloyd's Banking Group

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Harvard Business School

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Decision Science Analytics @ USAA

Ines Montani
Ines Montani

spaCy core developer and co-founder of Explosion AI

Ines is a developer specialising in applications for AI, Machine Learning and Natural Language Processing technologies. She's the co-founder of Explosion AI and a core developer of the spaCy NLP library, and Prodigy, an annotation tool for radically efficient machine teaching.

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Collaborators
  • Mari Nazary

    Mari Nazary

  • Adrián Soto

    Adrián Soto

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