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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.
Finding words, phrases, names and conceptsFree
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.Introduction to spaCy50 xpGetting Started100 xpDocuments, spans and tokens100 xpLexical attributes100 xpStatistical models50 xpModel packages50 xpLoading models100 xpPredicting linguistic annotations100 xpPredicting named entities in context100 xpRule-based matching50 xpUsing the Matcher100 xpWriting match patterns100 xp
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.Data Structures (1)50 xpStrings to hashes100 xpVocab, hashes and lexemes50 xpData Structures (2)50 xpCreating a Doc100 xpDocs, spans and entities from scratch100 xpData structures best practices100 xpWord vectors and similarity50 xpInspecting word vectors100 xpComparing similarities100 xpCombining models and rules50 xpDebugging patterns (1)50 xpDebugging patterns (2)100 xpEfficient phrase matching100 xpExtracting countries and relationships100 xp
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.Processing pipelines50 xpWhat happens when you call nlp?50 xpInspecting the pipeline100 xpCustom pipeline components50 xpUse cases for custom components50 xpSimple components100 xpComplex components100 xpExtension attributes50 xpSetting extension attributes (1)100 xpSetting extension attributes (2)100 xpEntities and extensions100 xpComponents with extensions100 xpScaling and performance50 xpProcessing streams100 xpProcessing data with context100 xpSelective processing100 xp
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.Training and updating models50 xpPurpose of training50 xpCreating training data (1)100 xpCreating training data (2)100 xpThe training loop50 xpSetting up the pipeline100 xpBuilding a training loop100 xpExploring the model100 xpTraining best practices50 xpGood data vs. bad data100 xpTraining multiple labels100 xpWrapping up50 xp
In the following tracksNatural Language Processing in Python
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.