머신러닝

1.Machine Learning

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2.Clustering

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3.K-means clustering

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4.Train / Validation / Test Data & 일반화

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5.선형회귀(Linear Regression)

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6.회귀 모델의 평가 지표

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7.Categorical Feature Encoing Methods

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8.Feature Engineering - SelectKBest

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9.Ridge Regression, RidgeCV

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10.Logistic Regression

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11.Tree model - Decision Tree

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12.Ensemble model - Random-Forests

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13.분류 모델의 평가 지표

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14.Model Selection : K-fold Cross Validation

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15.특성 중요도(Feature Importances)

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16.Ensemble model - Grandient Boosting

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