본문으로 바로가기

강의

spaCy로 배우는 자연어 처리

중급기술 수준

업데이트됨 2025. 7.

spaCy의 핵심 기능을 숙달하고 자연어 처리 모델을 훈련하세요. 구조화되지 않은 데이터에서 정보를 추출하고 패턴을 일치시킵니다.

무료로 강의 시작

PythonMachine Learning

4시간

15 동영상

53 연습 문제

4,450 XP

8,268

성취 증명서

수천 개 기업의 학습자들이 사랑하는

팀을 교육하시나요?

비즈니스용으로 체험해 보세요

강의 설명

토큰화, 파싱, 개체명 인식 같은 작업을 위해 빠르게 성장한 업계 표준 NLP 라이브러리인 spaCy를 배워 보세요. Doc, Token 같은 클래스를 활용해 핵심 작업을 익히고, 모델을 학습합니다. 패턴 매칭으로 용어를 추출하고, 사용자 지정 파이프라인 구성 요소를 만들며, 실제 사례를 다루어 NLP 프로젝트에 적용해 보세요.

선수 조건

Supervised Learning with scikit-learn Python Toolbox

1

Introduction to NLP and spaCy

This chapter will introduce you to NLP, some of its use cases such as named-entity recognition and AI-powered chatbots. You’ll learn how to use the powerful spaCy library to perform various natural language processing tasks such as tokenization, sentence segmentation, POS tagging, and named entity recognition.

Natural Language Processing (NLP) basics

Doc container in spaCy

NER use case

Tokenization with spaCy

spaCy basics

Running a spaCy pipeline

Lemmatization with spaCy

Sentence segmentation with spaCy

Linguistic features in spaCy

POS tagging with spaCy

NER with spaCy

Text processing with spaCy

2

spaCy Linguistic Annotations and Word Vectors

Learn about linguistic features, word vectors, semantic similarity, analogies, and word vector operations. In this chapter you’ll discover how to use spaCy to extract word vectors, categorize texts that are relevant to a given topic and find semantically similar terms to given words from a corpus or from a spaCy model vocabulary.

Linguistic features

Linguistic annotations in spaCy

Word-sense disambiguation with spaCy

Dependency parsing with spaCy

Introduction to word vectors

spaCy vocabulary

Word vectors in spaCy vocabulary

Word vectors and spaCy

Analogies and vector operations

Word vectors projection

Similar words in a vocabulary

Measuring semantic similarity with spaCy

Doc similarity with spaCy

Span similarity with spaCy

Semantic similarity for categorizing text

3

Data Analysis with spaCy

Get familiar with spaCy pipeline components, how to add a pipeline component, and analyze the NLP pipeline. You will also learn about multiple approaches for rule-based information extraction using EntityRuler, Matcher, and PhraseMatcher classes in spaCy and RegEx Python package.

spaCy pipelines

Adding pipes in spaCy

Analyzing pipelines in spaCy

spaCy EntityRuler

EntityRuler with blank spaCy model

EntityRuler for NER

EntityRuler with multi-patterns in spaCy

RegEx with spaCy

RegEx in Python

RegEx with EntityRuler in spaCy

spaCy Matcher and PhraseMatcher

Matching a single term in spaCy

PhraseMatcher in spaCy

Matching with extended syntax in spaCy

4

Customizing spaCy Models

Explore multiple real-world use cases where spaCy models may fail and learn how to train them further to improve model performance. You’ll be introduced to spaCy training steps and understand how to train an existing spaCy model or from scratch, and evaluate the model at the inference time.

Customizing spaCy models

Training spaCy models

Model performance on your data

spaCy training data format

Training steps

Annotation and preparing training data

Compatible training data

Training with spaCy

Training preparation steps

Train an existing NER model

Training a spaCy model from scratch

spaCy로 배우는 자연어 처리

강의
완료

수료증 획득

LinkedIn 프로필, 이력서 또는 CV에 이 인증서를 추가하세요
소셜 미디어와 성과 평가에서 공유하세요지금 등록

19백만 명 이상의 학습자와 함께 spaCy로 배우는 자연어 처리을(를) 시작하세요!

DataCamp for Mobile을 통해 데이터 분석 능력을 향상시키세요.

모바일 강좌와 매일 5분 코딩 챌린지를 통해 이동 중에도 학습 효과를 높이세요.