Learn SPACY with Real Code Examples

Learn Python basics

Understand NLP concepts: tokens, POS, entities

Practice using spaCy pipelines and pre-trained models

Explore custom components and rule-based matching

Integrate with ML/DL frameworks for NLP tasks

Week 1: tokenization, lemmatization, and POS tagging

Week 2: NER and dependency parsing

Week 3: custom pipeline components and matcher usage

Week 4: integration with ML models and vector similarity

Week 5: large-scale text processing and deployment pipelines

What are Doc, Token, and Span objects?

Explain spaCy pipelines and components

How do you train a custom NER model?

Difference between rule-based and ML-based pipelines

How to optimize spaCy for large-scale text processing?

nlp = spacy.load('en_core_web_sm') = load model

doc = nlp('text') = process text

token.text / token.pos_ = token attributes

[(ent.text, ent.label_) for ent in doc.ents] = extract entities

Matcher / PhraseMatcher = rule-based pattern matching

Natural Language Processing with spaCy by Yuli Vasiliev

Mastering spaCy by Duygu Altinok

Hands-On NLP with Python and spaCy

Practical Natural Language Processing with spaCy

Industrial-Strength NLP with spaCy

spaCy official tutorials

DataCamp NLP with spaCy

YouTube spaCy courses

Medium blog tutorials on spaCy

MOOCs on industrial NLP pipelines

https://spacy.io/

https://spacy.io/usage

https://github.com/explosion/spaCy

spaCy GitHub

StackOverflow spaCy tag

Reddit /r/LanguageTechnology

spaCy discussion forum

Online tutorials and blogs

spaCy GitHub repository

StackOverflow spaCy tag

Reddit /r/LanguageTechnology

spaCy discussion forums

Online tutorials and MOOCs