├── 1장 ├── 1.3 넘파이.ipynb └── 1.4 데이터 핸들링 - 판다스.ipynb ├── 2장 ├── 2.2 첫 번째 머신러닝 만들어 보기 _ 붓꽃 품종 예측하기.ipynb ├── 2.3 사이킷런의 기반 프레임워크 익히기.ipynb ├── 2.4 Model Selection 모듈 소개.ipynb ├── 2.5 데이터_전처리.ipynb └── 2.6 사이킷런으로 수행하는 타이타닉 생존자 예측 .ipynb ├── 3장 ├── 3.1정확도(Accuracy) ~ 3-5_ROC_AUC까지 예제.ipynb └── 3.6 피마 인디언 당뇨병 예측.ipynb ├── 4장 ├── 4.10 스태킹 앙상블.ipynb ├── 4.2 결정 트리.ipynb ├── 4.2 결정 트리_Ver01.ipynb ├── 4.3_앙상블학습_4.4_랜덤포레스트_4.5_GBM.ipynb ├── 4.3_앙상블학습_4.4_랜덤포레스트_4.5_GBM_Ver01.ipynb ├── 4.6 XGBoost(eXtra Gradient Boost).ipynb ├── 4.7 LightGBM.ipynb ├── 4.8 분류실습 _ 산탄데르 고객 만족 예측.ipynb └── 4.9 분류 실습-신용카드_사기검출.ipynb ├── 5장 ├── 5.10 Regression 실습 - Kaggle House Price.ipynb ├── 5.3_Gradient_Descent_5.4_LinearModel_5.5_Polynomial_5.6_Regularized_model.ipynb ├── 5.7_로지스틱 회귀_5.8_회귀 트리.ipynb └── 5.9 Regression실습-Bike Sharing Demand.ipynb ├── 6장 ├── 6-2_PCA.ipynb ├── 6-3_LDA(Linear Discriminant Analysis).ipynb └── 6-4_6_5_SVD & NMF.ipynb ├── 7장 ├── 7-1_KMeans.ipynb ├── 7-2_Cluster evaluation.ipynb ├── 7-3_Mean_Shift.ipynb ├── 7-4_Gaussian_Mixture_Model.ipynb ├── 7-5_DBSCAN.ipynb └── 7-6_Clustering_Practice_Customer_Segmentation.ipynb ├── 8장 ├── 8.10 Text Analysis 실습 _ 캐글 Mercari Price Suggestion Challenge.ipynb ├── 8.2 텍스트 사전 준비 작업(텍스트 전처리) - 텍스트 정규화_8.3 Bag of Words _ BOW.ipynb ├── 8.4 텍스트 분류 실습 _ 20 뉴스그룹 분류.ipynb ├── 8.5 감성 분석.ipynb ├── 8.6 토픽 모델링(Topic Modeling) - 20 뉴스그룹.ipynb ├── 8.7 문서 군집화 소개와 실습(Opinion Review 데이터 세트).ipynb ├── 8.8 문서 유사도 .ipynb └── 8.9 한글 텍스트 처리 _ 네이버 영화 평점 감성 분석.ipynb └── 9장 ├── 9.4 잠재 요인 협업 필터링.ipynb ├── 9.5 컨텐츠 기반 필터링 실습 _ TMDB 5000 Movie Dataset.ipynb ├── 9.6 아이템 기반 인접 이웃 협업 필터링 실습.ipynb ├── 9.7 행렬 분해 기반의 잠재 요인 협업 필터링 실습.ipynb └── 9.8 파이썬 추천 시스템 패키지 - Surprise.ipynb /1장/1.3 넘파이.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/wikibook/ml-definitive-guide/HEAD/1장/1.3 넘파이.ipynb -------------------------------------------------------------------------------- /1장/1.4 데이터 핸들링 - 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