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<title>Software Carpentry: 데이터 과학 – 모형</title>
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<h1 class="title">데이터 과학 – 모형</h1>
<aside class="callout panel panel-info">
<section id="기계와의-경쟁을-준비하며-ai-is-a-superpower" class="panel-heading">
<h3><span class="glyphicon glyphicon-pushpin"></span>기계와의 경쟁을 준비하며… AI is a Superpower</h3>
</section>
<div class="panel-body">
<p>“고대에는 ’땅’이 가장 중요했고 땅이 소수에게 집중되자 인간은 귀족과 평민으로 구분됐으며, 근대에는 ’기계’가 중요해지면서 기계가 소수에게 집중되자 인간은 자본가와 노동자 계급으로 구분됐다”. 이제는 <strong>데이터</strong>가 또 한번 인류를 구분하는 기준이 될 것이다. 향후 데이터가 소수에게 집중되면 단순 계급에 그치는 게 아니라 데이터를 가진 종과 그렇지 못한 종으로 분류될 것이이다. <a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a></p>
<p> - <a href="https://www.youtube.com/watch?v=7Xs3auqcX7k">유발 하라리(Yuval Noah Harari)</a></p>
<p>“AI is a superpower!!!”, 인공지능을 체득하면 슈퍼파워를 손에 쥘 것이다.</p>
<p> - <a href="https://twitter.com/andrewyng/status/728986380638916609">Andrew Ng</a></p>
<p>금수저, 은수저 슈퍼파워를 받은 사람과 기계학습을 통달한 흑수저들간의 무한경쟁이 드뎌 시작되었다. 물론, 금수저를 입에 물고 기계학습을 통달한 사람이 가장 유리한 출발을 시작한 것도 사실이다.</p>
<p>“The future is here, it’s just not evenly distributed yet.”</p>
<p> - William Gibson</p>
</div>
</aside>
<h2 id="학습목차">학습목차</h2>
<div class = "row">
<div class="col-md-6">
<p><strong>모형-I</strong></p>
<ul>
<li><a href="model-ml-intro.html">기계학습 개요</a>
<ul>
<li><a href="ml-r-design-matrix.html">예측모형 파이프 - R 모형설계행렬(Recipe)</a></li>
</ul></li>
<li><a href="model-data-cleaning.html"><strong>데이터 정제(Data Cleaning)</strong></a></li>
<li><a href="model-data-quality.html">데이터 품질(Data Quality)</a></li>
<li>모형 데이터 전처리 - Feature Engineering(피처 공학)
<ul>
<li><a href="model-class-imbalance.html">클래스 불균형(Class imbalance)</a></li>
<li><a href="model-feature-engineering.html">피처 공학(Feature Engineering)</a></li>
<li><a href="model-feature-engineering-tech.html">피처 공학 기법 - 직사각형 데이터프레임</a></li>
<li><a href="model-feature-engineering-selection.html">피처 공학 - 선택 혹은 추출</a></li>
<li><a href="model-feature-engineering-automation.html">피처 공학 - 변수선택 자동화</a></li>
</ul></li>
<li>탐색적 데이터분석(EDA)
<ul>
<li><a href="model-eda-wine.html">지도학습모형 → EDA - 포도주(<code>wine</code>)</a></li>
</ul></li>
<li><strong><a href="tidyverse-model.html"><code>tidyverse</code> 모형 - <code>tidymodels</code>: <code>tidyverse</code> 성명서</a></strong>
<ul>
<li><a href="tidyverse-model-helloworld.html"><code>tidyverse</code> 모형 헬로월드</a>**</li>
<li><a href="tidyverse-parsnip.html"><code>caret</code> → <code>parsnip</code></a></li>
<li><a href="tidyverse-parsnip-advanced.html">임직원 이탈 예측: <code>tidymodel</code></a>: 2020-07-20</li>
<li><a href="model_tree_tidymodels.html"><code>tidymodels</code>: 나무모형</a>, <a href="model_tree.html">나무모형 예측모형(CART, RF, …, SGBM)</a>
<ul>
<li><a href="model_survival_tree.html">나무모형과 생존분석의 만남</a></li>
<li><a href="model_geospatial_taxi.html">나무모형과 지리정보의 만남 - 택시인기 지점 예측</a></li>
</ul></li>
<li><a href="model-pokemon-unsupervised.html">포켓몬 PCA</a><br />
</li>
<li><a href="tidyverse-parsnip-penguin-101.html">펭귄 성별예측모형</a><br />
</li>
<li><a href="tidyverse-parsnip-penguin.html">펭귄 성별예측모형: <code>tidymodels</code></a><br />
</li>
<li><a href="tidyverse-parsnip-penguin-hyper-parameter.html">펭귄 성별예측모형: <code>tidymodels</code> - Hyper Parameter</a></li>
<li><a href="tidyverse-parsnip-penguin-usemodels.html">펭귄 성별예측모형: <code>tidymodels</code> + <code>usemodels</code></a></li>
<li><a href="tidyverse-parsnip-penguin-xgboost.html">펭귄 성별예측모형: <code>tidymodels</code> - <code>XGBoost</code></a></li>
<li><a href="tidyverse-parsnip-DALEX.html"><code>tidymodels</code> - MDP / DALEX</a>
<ul>
<li><a href="tidyverse-parsnip-penguin-DALEX.html">펭귄 성별예측모형 설명: <code>tidymodels</code> - MDP / DALEX</a></li>
</ul></li>
<li><a href="model-tidyposterior.html">펭귄 성별예측모형: <code>tidyposterior</code></a></li>
<li><strong>배포(Deployment)</strong>
<ul>
<li><a href="tidyverse-parsnip-penguin-shiny.html">펭귄 성별예측모형: <code>tidymodels</code> - <code>Shiny</code></a></li>
<li><a href="tidyverse-parsnip-penguin-RESTful-api.html">펭귄 성별예측모형: <code>tidymodels</code> - RESTful API</a></li>
<li><a href="tidyverse-penguin-python-shiny.html">펭귄 성별예측모형: 파이썬 + <code>Shiny</code></a></li>
</ul></li>
</ul></li>
<li><strong>GDPR</strong>
<ul>
<li><a href="model-gdpr-fine.html">GDPR 벌금 예측모형: EDA</a></li>
<li><a href="model-gdpr-challenge.html">예측모형 GDPR: 설명가능한 특정 모형</a></li>
<li><a href="model-gdpr-regression.html">예측모형 GDPR: <code>tidymodels</code></a>: 2020-07-21</li>
<li><a href="tidyverse-parsnip-textrecipes.html">책 저자 분류모형: <code>parsnip</code> + <code>tidytext</code> + <code>textrecipes</code></a>
<ul>
<li><a href="model-explain.html">GDPR powered by <code>tidymodels</code>, <code>shiny</code>, <code>dalex</code>, <code>plumber</code></a></li>
</ul></li>
<li><a href="model-r2d3-dalex-with-biz.html">예측모형 (caret+DALEX+biz) - 뉴욕과 SF 부동산</a>
<ul>
<li><a href="model-mpg-dalex.html">DALEX - mpg(연비 데이터)</a></li>
<li><a href="model-r2d3-dalex.html">DALEX - R2D3, 뉴욕과 SF 부동산 분류 데이터</a></li>
<li><a href="model-r2d3-caret-dalex.html">DALEX + <code>caret</code> - R2D3, 뉴욕과 SF 부동산 분류 데이터</a></li>
<li><a href="model-h2o-dalex.html">DALEX - <span class="math inline">\(H_2O\)</span>, <code>mpg</code>와 <code>attrition</code> 데이터</a></li>
</ul></li>
</ul></li>
<li>모형 시각화
<ul>
<li><a href="model_purrr_trelliscopejs.html">회귀모형 - <code>purrr</code> + <code>trelliscopejs</code></a></li>
<li><a href="model_tsne_mtcars.html">비지도학습 tsne - <code>mtcars</code></a></li>
<li><a href="model_kidney-lime.html">만성 신부전증(Kidney) 예측 - LIME</a></li>
</ul></li>
<li>모형 평가
<ul>
<li><a href="model-business-value.html">예측모형 가치(Business Value)</a></li>
</ul></li>
<li><strong>모형 자동화(AutoML)</strong>
<ul>
<li><a href="model-h2o-automl.html">순수 <span class="math inline">\(H_2 O\)</span> AutoML</a></li>
<li><a href="model-dplyr-h2o-automl.html"><code>dplyr</code> + <span class="math inline">\(H_2 O\)</span> AutoML</a></li>
<li><a href="model-recipe-h2o-automl.html">기계학습 모형개발 30분 - <code>recipe</code> + <span class="math inline">\(H_2 O\)</span> AutoML</a></li>
<li><a href="model-deploy.html">기계학습 모형 배포</a></li>
<li><a href="model-ensemble.html"><strong>앙상블(ensemble) 모형</strong></a></li>
</ul></li>
<li><strong>실무 모형</strong>
<ul>
<li><strong>사기 탐지(Fraud Detection)</strong>
<ul>
<li><a href="https://statkclee.github.io/ml/ml-detect-outliers-mahalanobis.html">단변량/다변량 이상점 검출</a></li>
<li><a href="model-anomaly.html">어노말리(Anomaly) 탐지</a></li>
</ul></li>
<li><strong><a href="credit-scoring-model.html">신용평점모형 개발</a></strong></li>
</ul>
</div>
<div class = "col-md-6">
</li>
</ul>
<p><strong>모형-II</strong></p>
<ul>
<li><strong>Oldest but Goodies - <code>caret</code></strong>
<ul>
<li><a href="model-caret-intro.html"><code>caret</code> 예측모형 맛보기</a></li>
<li><a href="model-caret-build.html"><code>caret</code> 예측모형 개발</a></li>
<li><a href="model-hyper-parameter.html">초모수 미세조정(Hyper Parameter Tuning)</a><br />
</li>
<li><a href="model-caret-in-practice.html"><code>caret</code> 예측모형 실전코드</a></li>
<li><a href="model-tictactoe-parsnip.html">틱택토(Tic-Tac-Toe) - <code>parsnip</code></a></li>
<li><a href="model_svm.html">서포트 벡터 머신(SVM)</a></li>
<li><a href="model-glm-testing.html">통계검정 → GLM</a><br />
</li>
</ul></li>
<li><strong>Many Models</strong>
<ul>
<li><a href="model-ml-purrr.html">기계학습 - <code>gapminer</code> + <code>rsample</code> + <code>purrr</code></a></li>
<li><a href="tidyverse-purrr-many-models.html"><code>purrr</code> - 많은 모형(many models)</a></li>
</ul></li>
<li><strong>데이터베이스와 깔끔한 모형</strong>
<ul>
<li><a href="model-database-dplyr.html">데이터베이스 - <code>dplyr</code></a></li>
</ul></li>
<li><strong>모형 인프라(Model Infrastructure)</strong>
<ul>
<li><a href="model-rsampling.html"><code>rsampling</code></a></li>
<li><a href="model-rsampling-time-series.html">시계열 데이터 - 항공여객(Air Passenger) 데이터</a></li>
<li><a href="model_rsample-arima.html">항공여객 데이터 ARIMA 모형 - <code>rsample</code></a><br />
</li>
</ul></li>
<li><strong><a href="model-cloud-infra.html">클라우드 컴퓨팅 환경</a></strong>
<ul>
<li><a href="model-aws-ec2.html">예측모형 AWS EC2</a></li>
</ul></li>
<li><strong><a href="model-cloudera.html">클라우데라 설명가능한 기계학습</a></strong>
<ul>
<li><a href="model-cloudera-logistic.html">고객이탈 - 로지스틱 회귀모형</a></li>
<li><a href="model-cloudera-rf.html">고객이탈 - Random Forest</a></li>
<li><a href="model-cloudera-lime.html">고객이탈 - DALEX + LIME</a></li>
<li><a href="model-cloudera-tidymodels.html">고객이탈 - <code>tidymodels</code></a></li>
<li><a href="model-cloudera-plumber.html">고객이탈 - RESTful API 기본기 <code>plumber</code></a></li>
<li><a href="model-cloudera-plumber-api.html">고객이탈 - RESTful API 모형 배포 <code>plumber</code></a></li>
<li><a href="model-cloudera-plumber-docker.html">고객이탈 - RESTful API 모형 배포 도커</a></li>
</ul></li>
<li><strong>파이썬 실무 예측모형</strong>
<ul>
<li><a href="model-python-wine.html">파이썬 + R - 포도주 품질</a></li>
<li><a href="model-python-churn.html">파이썬 고객이탈 - <code>xgBoost</code></a>
<ul>
<li><a href="model-python-xgboost-hyper.html">파이썬 고객이탈 - <code>XGBoost</code> 초모수 튜닝</a></li>
</ul></li>
<li><a href="model-python-predictive-model.html">파이썬 예측모형 - 시운전(Dry-Run)</a></li>
<li><a href="model-python-predictive-model-lifecycle.html">파이썬 예측모형 - 생애주기(lifecycle)</a></li>
<li><a href="model-python-cross-validation.html">파이썬 예측모형 - 교차검증(cross-validation)</a></li>
</ul></li>
<li><strong>데이터 결합(Data Fusion) - 네트워크, 텍스트, 이미지, 시계열</strong>
<ul>
<li><a href="model-kaggle-text.html">캐글 - 전자상거래 옷 리뷰</a></li>
<li><a href="model-network.html">예측모형 - 네트워크</a></li>
<li><a href="model-tsne.html">예측모형 - 데이터 융합(<code>tsne</code>)</a></li>
</ul>
</div></li>
</ul>
</div>
<section class="footnotes" role="doc-endnotes">
<hr />
<ol>
<li id="fn1" role="doc-endnote"><p><a href="http://news.mk.co.kr/newsRead.php?year=2018&no=58432">‘사피엔스’ 저자 유발 하라리 “인간을 해킹하는 시대가 온다”, “머신러닝·AI·생물학 발전…뇌과학 이해도 한층 높여”</a><a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
</ol>
</section>
</div>
</div>
</article>
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