03 machine learning/01.02 머신러닝용 파이썬 패키지.html


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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
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   2장 파이썬 기초문법
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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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     3.4 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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<p class="caption">
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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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     10.2 조건부엔트로피
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     10.4 상호정보량
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   이미지 특징 추출
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   사운드 프로세싱 기초
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   지리 정보 데이터 처리
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   4.1 회귀분석 예제
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   4.2 선형회귀분석의 기초
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   5.1 확률론적 선형 회귀모형
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   5.3 레버리지와 아웃라이어
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   6.1 모형 진단과 수정
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  <div class="section" id="id1">
<h1>1.2 머신러닝용 파이썬 패키지<a class="headerlink" href="#id1" title="Permalink to this headline">¶</a></h1>
<p>이 절에서는 머신러닝에서 많이 사용되는 파이썬 패키지를 소개한다. 이 패키지들은 교재의 예제 코드에서 사용되므로 미리 실치하기를 권장한다. pip 또는 conda 패키지 관리자로 설치할 수 있다.</p>
<div class="section" id="id2">
<h2>기본 패키지<a class="headerlink" href="#id2" title="Permalink to this headline">¶</a></h2>
<p>다음에 소개하는 패키지는 머신러닝 뿐 아니라 모든 종류의 데이터 분석 업무에 공통적으로 사용된다. 본 교재의 코드는 항상 이 패키지들을 임포트하고 있다고 가정한다. 자주 사용하는 패키지이므로 짧은 별명(alias)으로 임포트하여 사용하는 경우가 많다.</p>
<div class="section" id="numpy">
<h3>numpy 패키지<a class="headerlink" href="#numpy" title="Permalink to this headline">¶</a></h3>
<p>numpy(“넘파이”라고 읽는다) 패키지는 파이썬에서 수치 해석, 특히 선형대수 계산 기능을 제공한다. 자료형이 고정된 다차원 배열 클래스(n-dimensional array)와 벡터화 연산(vectorized operation)을 지원하며 수학 연산에서 가장 기본적이고 중요한 패키지다</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">numpy</span>
</pre></div>
</div>
<p>임포트할 때는 보통 <code class="docutils literal notranslate"><span class="pre">np</span></code>라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
</pre></div>
</div>
</div>
<div class="section" id="scipy">
<h3>scipy 패키지<a class="headerlink" href="#scipy" title="Permalink to this headline">¶</a></h3>
<p>scipy(“사이파이”라고 읽는다) 패키지는 고급 수학 함수, 수치적 미적분, 미분 방정식 계산, 최적화, 신호 처리 등에 사용하는 다양한 과학 기술 계산 기능을 제공한다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">scipy</span>
</pre></div>
</div>
<p>임포트할 때는 보통 <code class="docutils literal notranslate"><span class="pre">sp</span></code>라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">scipy</span> <span class="k">as</span> <span class="nn">sp</span>
</pre></div>
</div>
</div>
<div class="section" id="pandas">
<h3>pandas 패키지<a class="headerlink" href="#pandas" title="Permalink to this headline">¶</a></h3>
<p>pandas(“판다스” 또는 “팬더스”라고 읽는다) 패키지는 테이블 형태의 데이터를 다루는 데이터프레임(DataFrame) 자료형을 제공한다. 자료 탐색과 정리에 아주 유용하여 데이터 분석에 빠질 수 없는 필수 패키지다. 원래는 R 언어에서 제공하는 데이터프레임 자료형을 파이썬에서 제공할 목적이었으나 더 다양한 기능이 추가되었다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">pandas</span>
</pre></div>
</div>
<p>임포트할 때는 보통 <code class="docutils literal notranslate"><span class="pre">pd</span></code>라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
</pre></div>
</div>
</div>
<div class="section" id="matplotlib">
<h3>matplotlib 패키지<a class="headerlink" href="#matplotlib" title="Permalink to this headline">¶</a></h3>
<p>matplotlib(“맷플롯립”이라고 읽는다) 패키지는 파이썬에서 각종 그래프나 챠트 등을 그리는 시각화 기능을 제공한다. Tkinter, wxPython, Qt, GTK+ 등의 다양한 그래픽 엔진을 사용할 수 있다. 또한 MATLAB의 그래프 기능을 거의 동일하게 사용할 수 있는 pylab이라는 서브패키지를 제공하므로 MATLAB에 익숙한 사람들은 바로 맷플롯립을 사용할 수 있다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">matplotlib</span>
</pre></div>
</div>
<p>임포트할 때는 보통 pylab 서브패키지를 <code class="docutils literal notranslate"><span class="pre">plt</span></code>라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">matplotlib.pylab</span> <span class="k">as</span> <span class="nn">plt</span>
</pre></div>
</div>
</div>
<div class="section" id="seaborn">
<h3>seaborn 패키지<a class="headerlink" href="#seaborn" title="Permalink to this headline">¶</a></h3>
<p>seaborn(“시본”이라고 읽는다) 패키지는 맷플롯립 패키지에서 지원하지 않는 고급 통계 차트를 그리는 통계용 시각화 기능을 제공한다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">seaborn</span>
</pre></div>
</div>
<p>임포트할 때는 보통 <code class="docutils literal notranslate"><span class="pre">sns</span></code>라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">seaborn</span> <span class="k">as</span> <span class="nn">sns</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id3">
<h2>머신러닝 패키지<a class="headerlink" href="#id3" title="Permalink to this headline">¶</a></h2>
<div class="section" id="statsmodels">
<h3>statsmodels 패키지<a class="headerlink" href="#statsmodels" title="Permalink to this headline">¶</a></h3>
<p>statsmodels(“스탯츠모델즈”라고 읽는다) 패키지는 추정 및 검정, 회귀분석, 시계열분석 등의 기능을 제공하는 파이썬 패키지다. 기존에 R에서 가능했던 다양한 회귀분석과 시계열분석 방법론을 그대로 파이썬에서 이용할 수 있다. 다음은 statsmodels 패키지가 제공하는 기능의 일부다.</p>
<ul class="simple">
<li><p>예제 데이터셋</p></li>
<li><p>검정 및 모수추정</p></li>
<li><p>회귀분석</p></li>
<li><p>선형회귀</p></li>
<li><p>강건회귀</p></li>
<li><p>일반화 선형모형</p></li>
<li><p>혼합효과모형</p></li>
<li><p>이산종속변수</p></li>
<li><p>시계열 분석</p></li>
<li><p>SARIMAX 모형</p></li>
<li><p>상태공간 모형</p></li>
<li><p>벡터 AR 모형</p></li>
<li><p>생존분석</p></li>
<li><p>요인분석</p></li>
</ul>
<p>교재에서는 선형회귀분석, 로지스틱회귀분석, 시계열분석에서 statsmodels 패키지를 사용한다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">statsmodels</span>
</pre></div>
</div>
<p>임포트할 때는 보통 api 서브패키지를 <code class="docutils literal notranslate"><span class="pre">sm</span></code>이라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">statsmodels.api</span> <span class="k">as</span> <span class="nn">sm</span>
</pre></div>
</div>
</div>
<div class="section" id="scikit-learn">
<h3>scikit-learn 패키지<a class="headerlink" href="#scikit-learn" title="Permalink to this headline">¶</a></h3>
<p>scikit-learn(“사이킷런”이라고 읽는다) 패키지는 머신러닝 교육을 위한 최고의 파이썬 패키지다. scikit-learn 패키지의 장점은 다양한 머신러닝 모형을 하나의 패키지에서 모두 제공하고 있다는 점이다. 다음은 scikit-learn 패키지에서 제공하는 머신러닝 모형의 목록의 일부다.</p>
<ul class="simple">
<li><p>데이터셋</p>
<ul>
<li><p>회귀분석, 분류, 클러스터링용 가상 데이터셋 생성</p></li>
<li><p>각종 벤치마크 데이터셋</p></li>
</ul>
</li>
<li><p>전처리</p>
<ul>
<li><p>스케일링</p></li>
<li><p>누락데이터 처리</p></li>
<li><p>텍스트 토큰화</p></li>
</ul>
</li>
<li><p>지도학습</p>
<ul>
<li><p>회귀분석</p></li>
<li><p>LDA/QDA</p></li>
<li><p>서포트벡터머신</p></li>
<li><p>퍼셉트론, SGD</p></li>
<li><p>KNN</p></li>
<li><p>가우스프로세스</p></li>
<li><p>나이브베이즈</p></li>
<li><p>의사결정나무</p></li>
<li><p>랜덤포레스트, 부스팅</p></li>
</ul>
</li>
<li><p>비지도학습</p>
<ul>
<li><p>가우스 혼합모형</p></li>
<li><p>클러스터링</p></li>
<li><p>PCA</p></li>
</ul>
</li>
<li><p>성능 최적화</p></li>
<li><p>교차검증</p></li>
<li><p>특징선택</p></li>
<li><p>하이퍼파라미터 최적화</p></li>
</ul>
<p>설치와 임포트에 사용하는 이름은 sklearn이다. 다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">sklearn</span>
</pre></div>
</div>
<p>임포트할 때는 보통 <code class="docutils literal notranslate"><span class="pre">sk</span></code>이라는 별명으로 임포트한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">sklearn</span> <span class="k">as</span> <span class="nn">sk</span>
</pre></div>
</div>
</div>
</div>
<div class="section" id="id4">
<h2>데이터 전처리용 패키지<a class="headerlink" href="#id4" title="Permalink to this headline">¶</a></h2>
<div class="section" id="missingno">
<h3>missingno 패키지<a class="headerlink" href="#missingno" title="Permalink to this headline">¶</a></h3>
<p>pandas 데이터프레임 데이터에서 누락된 데이터를 찾고 시각화하는 기능을 제공한다.</p>
<p>다음 명령으로 설치한다.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">pip</span> <span class="n">install</span> <span class="n">missingno</span>
</pre></div>
</div>
</div>
<div class="section" id="patsy">
<h3>patsy 패키지<a class="headerlink" href="#patsy" title="Permalink to this headline">¶</a></h3>
<p>pandas 데이터프레임 데이터에서 선택, 변형하는 기능을 제공한다.</p>
<p>statsmodels가 의존하는 패키지이므로 statsmodels 패키지를 설치하면 별도로 설치할 필요가 없다.</p>
</div>
</div>
<div class="section" id="id5">
<h2>텍스트 전처리용 패키지<a class="headerlink" href="#id5" title="Permalink to this headline">¶</a></h2>
<div class="section" id="nltk">
<h3>nltk 패키지<a class="headerlink" href="#nltk" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="spacy">
<h3>spacy 패키지<a class="headerlink" href="#spacy" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="konlpy">
<h3>konlpy 패키지<a class="headerlink" href="#konlpy" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="soynlp">
<h3>soynlp 패키지<a class="headerlink" href="#soynlp" title="Permalink to this headline">¶</a></h3>
</div>
<div class="section" id="gensim">
<h3>gensim 패키지<a class="headerlink" href="#gensim" title="Permalink to this headline">¶</a></h3>
</div>
</div>
<div class="section" id="id6">
<h2>이미지 전처리용 패키지<a class="headerlink" href="#id6" title="Permalink to this headline">¶</a></h2>
<div class="section" id="opencv">
<h3>opencv 패키지<a class="headerlink" href="#opencv" title="Permalink to this headline">¶</a></h3>
</div>
</div>
<div class="section" id="id7">
<h2>사운드 전처리용 패키지<a class="headerlink" href="#id7" title="Permalink to this headline">¶</a></h2>
<div class="section" id="librosa">
<h3>librosa 패키지<a class="headerlink" href="#librosa" title="Permalink to this headline">¶</a></h3>
</div>
</div>
<div class="section" id="id8">
<h2>지리정보 전처리용 패키지<a class="headerlink" href="#id8" title="Permalink to this headline">¶</a></h2>
<div class="section" id="geopandas">
<h3>geopandas 패키지<a class="headerlink" href="#geopandas" title="Permalink to this headline">¶</a></h3>
</div>
</div>
</div>

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