Supervised Learning
25 videos • 2,110 views • by Spencer Pao
1
Applying and Understanding K-Nearest Neighbors (KNN) in R
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2
HOW TO: Linear Regression in RStudio
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3
HOW TO: Multiple Linear Regression -- Analysis -- Part 1
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4
HOW TO: Multiple Linear Regression --Modeling-- Part 2
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5
Understanding Logistic Regression in R
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6
Applying Logistic Regression in R
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7
Ridge Regression Theory
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8
Applied Ridge Regression in R part 1
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9
Applied Ridge Regression in R part 2
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10
Lasso Regression Theory
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11
Applied Lasso Regression in R
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12
Elastic Net Regression Theory
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13
Applied Elastic Net Regression in R
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14
Applying Random Forests and Decision Trees in R pt. 1
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15
Applying Random Forests and Decision Trees in R pt. 2
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16
Applied Principal Component Analysis in R
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17
Ensemble Method: Boosting (Hypothesis Boosting)
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18
Understanding and Applying Factor Analysis in R
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19
Understanding and Applying Vector Machines in R (SVM, LDA, QDA)
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20
Dealing with MISSING Data! Data Imputation in R (Mean, Median, MICE!)
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21
Bayesian Regression in R
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22
Sampling Data with SMOTE, Tomek Links, and Nearmiss in R
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23
Understanding and Applying XGBoost Classification Trees in R
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24
Understanding and Applying XGBoost Regression Trees in R
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25
Principal Component Regression in R
Spencer Pao
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