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Advanced NLP with spaCy
IntermediateSkill Level
Updated 11/2024
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PythonMachine Learning5 hr15 videos55 Exercises4,450 XP21,653Statement of Accomplishment
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Prerequisites
Introduction to Natural Language Processing in Python1
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.
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.
Advanced NLP with spaCy
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FAQs
What Python libraries and tools does this course focus on?
The course centers on spaCy, a leading industry library for natural language processing in Python. You use it for text processing, entity recognition, and training custom models.
Will I learn how to train custom NLP models with spaCy?
Yes. Chapter 4 teaches you to update spaCy statistical models for your own use case, including writing a training loop from scratch and predicting new entity types.
Does this course cover both rule-based and machine learning approaches?
Yes. You learn to combine rule-based pattern matching with statistical models for large-scale text analysis, giving you flexible tools for different NLP tasks.
What should I know before starting this course?
You need intermediate Python skills and familiarity with basic NLP concepts. Prior completion of an introductory NLP course and experience with Python functions is recommended.
How long does this course take to complete?
It contains 4 chapters with 55 exercises. The median completion time is about 4.5 hours, though individual pace may vary depending on your background.
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