Chapter 1: Finding words, phrases, names and concepts

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.

Chapter 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.

Chapter 3: Processing Pipelines

This chapter will show you 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 metadata to the documents, spans and tokens.

Chapter 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.

About this course

spaCy is a modern Python library for industrial-strength Natural Language Processing. In this free and interactive online course, you'll learn how to use spaCy to build advanced natural language understanding systems, using both rule-based and machine learning approaches.

About me

I'm Ines, one of the core developers of spaCy and the co-founder of Explosion. I specialize in modern developer tools for AI, Machine Learning and NLP. I also really love building stuff for the web.