Token Classification in NLP using spaCy | CodeSignal Learn

intermediate

Token Classification in NLP using spaCy

Natural Language Processing

3 courses

56 practices

7 hours

Delve into token classification in NLP with this focused learning path. This course centers on using spaCy for token classification, from setting up an NLP pipeline to advanced classification model fine-tuning. Upon completion, you'll be adept in utilizing spaCy for token classification in NLP.

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Earn a shareable

Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Linguistics and Semantics

INTERMEDIATE

Text Data Collection and Preparation

DEVELOPING

Machine Learning Modeling for NLP

Tools you'll use

NLTK

Python

spaCy

Trusted by learners working at top companies

Course 1

Building an NLP Pipeline with spaCy for Token Classification

5 lessons

25 practices

Kickstart your journey into token classification by setting up an efficient NLP pipeline, learning about tokenization, POS tagging, and lemmatization with spaCy.

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Course 2

Linguistics for Token Classification in spaCy

5 lessons

21 practices

Delve deeper into fully enhancing Token Classification by understanding linguistic and semantic aspects of Natural Language Processing. Gain grasp of language morphology and recognize entity types using spaCy.

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Course 3

Practical Applications of spaCy for Real-Life Tasks

3 lessons

10 practices

Understanding theory and basic tools of spaCy is of paramount importance, but applying them to real-life tasks is what drives value. The course focuses on four case studies that involve practical applications of spaCy to solve real-world problems.

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