# Scikit-learn Cookbook Trent Hauck (지은이) | Packt Pub Ltd | 2014-10-31 214쪽 | 235*191mm | 언어 : English | 국가 : 미국 | 244g | ISBN : 9781783989485 ![](https://d255esdrn735hr.cloudfront.net/sites/default/files/imagecache/dotd_main_image/9485OS_cov_0.jpg)(https://www.packtpub.com/packt/offers/free-learning) 해당 도서는 2014년에 출간된 1판 이며, 한글번역판은 출간되지 않았습니다.[(알라딘 참조)](http://www.aladin.co.kr/search/wsearchresult.aspx?SearchTarget=All&SearchWord=scikit-learn+Cookbook&x=25&y=6) 목차는 다음과 같습니다. 1: Premodel Workflow 2: Working with Linear Models 3: Building Models with Distance Metrics 4: Classifying Data with scikit-learn 5: Postmodel Workflow Appendix A: Index 관심있으신 분은 다음 주소타고 가셔서, 회원가입 후 24시간안에 받으시면 되겠습니다. [https://www.packtpub.com/packt/offers/free-learning](https://www.packtpub.com/packt/offers/free-learning) “로봇이 아닙니다.” 체크 하신후, Claim Your Free ebook를 클릭하시면 됩니다. 제공 포맷은 PDF, ePub, Mobi 입니다. 좋은 하루 보내세요. ## 참고자료 scikit-learn 소개:[위키피디어(영문)](http://scikit-learn.org/stable/) scikit-learn 안내:[공식사이트(영문)](http://scikit-learn.org/stable/),[Documentation(영문)](http://scikit-learn.org/stable/documentation.html),[github(영문)](https://github.com/scikit-learn/scikit-learn) scikit-learn을 활용한 기계 학습:[brenden17님 github(한글)](https://github.com/brenden17/blog/blob/master/post/ms.scikit-learn.v.md) Scikit-Learn Cheat Sheet: Python Machine Learning:[Datacamp(영문)](https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet#gs.MfSPyYs),[cheat-sheet(영문)](https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf) A Gentle Introduction to Scikit-Learn:[A Python Machine Learning Library(영문)](http://machinelearningmastery.com/a-gentle-introduction-to-scikit-learn-a-python-machine-learning-library/) ## 참고자료 - http://scikit-learn.org/stable/ - http://www.scipy.org/ - http://www.acornpub.co.kr/book/machine-learning-python - https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/neighbors/classification.py - http://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsClassifier.html - {{tag>machine learning scikit learn sklearn python}}