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08.01 사이파이를 이용한 확률분포 분석.html
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08.01 사이파이를 이용한 확률분포 분석.html
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<!DOCTYPE html>
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<title>8.1 사이파이를 이용한 확률분포 분석 — 데이터 사이언스 스쿨</title>
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<h1 class="site-logo" id="site-title">데이터 사이언스 스쿨</h1>
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데이터 사이언스 스쿨
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파이썬 편
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<a class="reference internal" href="../01%20python/00.00%20%EC%86%8C%EA%B0%9C%EC%9D%98%20%EA%B8%80.html">
소개의 글
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1장 파이썬 설치와 설정
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1.1 파이썬 설치하기
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1.2 파이썬 처음 사용하기
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1.3 파이썬 패키지 설치하기
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1.4 데이터 분석용 파이썬 패키지 소개
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1.5 아이파이썬 및 주피터 설정
</a>
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2장 파이썬 기초문법
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2.1 파이썬을 계산기로 사용하기
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<li class="toctree-l2">
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2.2 부동소수점 실수 자료형
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2.3 파이썬으로 글자를 출력하기
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2.4 파이썬의 문자열 형식화
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2.5 파이썬 조건문 기초
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2.6 파이썬 함수
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2.7 파이썬 for 반복문
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2.8 여러 개의 자료를 한 변수에 담기
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2.9 파이썬에서 리스트 자료형 다루기
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2.10 리스트와 반복문을 사용하여 계산하기
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2.11 파이썬에서 딕셔너리 자료형 다루기
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2.12 파이썬 객체지향 프로그래밍
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2.13 파이썬 패키지 사용하기
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2.14 파이썬의 자료형
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2.15 파이썬에서 날짜와 시간 다루기
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3장 넘파이 배열 프로그래밍
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3.1 넘파이 배열
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3.2 배열의 생성과 변형
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3.3 배열의 연산
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3.4 기술 통계
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3.5 난수 발생과 카운팅
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4장 판다스 데이터 분석
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4.1 판다스 패키지의 소개
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4.2 데이터 입출력
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4.3 데이터프레임 고급 인덱싱
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4.4 데이터프레임의 데이터 조작
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4.5 데이터프레임 인덱스 조작
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4.6 데이터프레임 합성
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4.7 피봇테이블과 그룹분석
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4.8 시계열 자료 다루기
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5장 데이터 시각화
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5.1 시각화 패키지 맷플롯리브 소개
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Matplotlib의 여러가지 플롯
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Matplotlib의 triangular grid 사용법
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Seaborn을 사용한 데이터 분포 시각화
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Pandas의 시각화 기능
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수학 편
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소개의 글
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1장 수학 기호
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1.1 그리스 문자
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1.2 수열과 집합의 합과 곱
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2장 넘파이(NumPy)로 공부하는 선형대수
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2.1 데이터와 행렬
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2.2 벡터와 행렬의 연산
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2.3 행렬의 성질
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2.4 선형 연립방정식과 역행렬
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3장 고급 선형대수
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3.1 선형대수와 해석기하의 기초
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3.2 좌표와 변환
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3.3 고윳값 분해
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3.4 특잇값 분해
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3.5 PCA
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4장 심파이(SymPy)로 공부하는 미적분
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4.1 함수
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4.2 심파이를 사용한 함수 미분
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4.3 적분
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4.4 행렬의 미분
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4.5 변분법
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5장 사이파이(SciPy)로 공부하는 최적화
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5.1 최적화 기초
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5.2 제한조건이 있는 최적화 문제
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5.3 선형계획법 문제와 이차계획법 문제
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6장 피지엠파이(pgmpy)로 공부하는 확률론
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6.1 집합
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6.2 확률의 수학적 정의와 의미
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6.3 확률의 성질
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6.4 확률분포함수
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6.5 결합확률과 조건부확률
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6.6 베이즈 정리
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7장 확률변수와 상관관계
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7.1 확률적 데이터와 확률변수
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7.2 기댓값과 확률변수의 변환
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7.3 분산과 표준편차
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7.4 다변수 확률변수
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7.5 공분산과 상관계수
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7.6 조건부기댓값과 예측 문제
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8장 사이파이로 공부하는 확률분포
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8.1 사이파이를 이용한 확률분포 분석
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8.2 베르누이분포와 이항분포
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8.3 카테고리분포와 다항분포
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8.4 정규분포와 중심극한정리
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8.5 스튜던트 t분포, 카이제곱분포, F분포
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8.6 다변수정규분포
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8.7 베타분포, 감마분포, 디리클레분포
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9장 추정과 검정
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9.1 확률분포의 추정
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9.2 최대가능도 추정법
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9.3 베이즈 추정법
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9.4 검정과 유의확률
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9.5 사이파이를 사용한 검정
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10장 엔트로피
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10.1 엔트로피
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10.2 조건부엔트로피
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10.3 교차엔트로피와 쿨백-라이블러 발산
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10.4 상호정보량
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머신러닝 편
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1.1 데이터 분석의 소개
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1.2 머신러닝용 파이썬 패키지
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2.1 데이터 전처리 기초
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2.2 범주형 데이터 처리
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NLTK 자연어 처리 패키지
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KoNLPy 한국어 처리 패키지
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Scikit-Learn의 문서 전처리 기능
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Soynlp 소개
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확률론적 언어 모형
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이미지 처리 기초
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이미지 필터링
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이미지 컨투어
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이미지 변환
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이미지 특징 추출
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사운드 프로세싱 기초
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푸리에 변환과 스펙트럼
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지리 정보 데이터 처리
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Geocoding
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4.1 회귀분석 예제
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4.2 선형회귀분석의 기초
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4.3 스케일링
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4.4 범주형 독립변수
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4.5 부분회귀
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5.1 확률론적 선형 회귀모형
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5.2 회귀분석의 기하학
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5.3 레버리지와 아웃라이어
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5.4 분산 분석과 모형 성능
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6.1 모형 진단과 수정
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6.2 기저함수 모형과 과최적화
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6.3 교차검증
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6.4 다중공선성과 변수 선택
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6.5 정규화 선형회귀
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13.1 추천 시스템
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5.1 분류용 예제 데이터
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5.2 분류용 가상 데이터 생성
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5.3 분류모형
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5.4 분류 성능평가
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6.1 로지스틱 회귀분석
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7.1 선형판별분석법과 이차판별분석법
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7.2 나이브베이즈 분류모형
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감성 분석
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8.1 의사결정나무
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12.02 모형 결합
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부스팅 방법
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서포트 벡터 머신
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커널 서포트 벡터 머신
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모형 최적화
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