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beane

Robbie Beane

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10.1 - Principal Component Analysis
10.1 - Principal Component Analysis
10.2 - PCA for Facial Recognition
8.2 - Random Forests
8.2 - Random Forests
8.1 - Decision Trees
8.1 - Decision Trees
5.2 - Introduction to Caret
5.2 - Introduction to Caret
5.1 - Introduction to Cross Validation
5.1 - Introduction to Cross Validation
6.2 - Regularized Logistic Regression
6.2 - Regularized Logistic Regression
6.1 - Ridge and LASSO Regression
Document
4.3 - KNN Classification
4.3 - KNN Classification
4.2 - Multinomial Logistic Regression
4.2 - Multinomial Logistic Regression
4.1 - Logistic Regression
4.1 - Logistic Regression
3.6 - Logarithmic Transformations
3.6 - Logarithmic Transformations
3.5 - Categorical Predictors
3.5 - Categorical Predictors
3.4 - Multiple Regression
3.4 - Multiple Regression
3.2 - Assumptions about Error Term
3.3 - Inference in SLR
3.2 - Assumptions about Error Term
3.3 - Inference in SLR
3.1.b - Derivation of R-Squared
3.1.a - Derivation of Parameter Estimates
2.3 - Bias-Variance Trade-Off
2.3 - Bias-Variance Trade-Off
3.1 -Simple Linear Regression
3.1 -Simple Linear Regression
2.2 - Training Regression Models
A04 - Random Sampling in R
A03 - Additional Vector Topics
A02 - Vectors in R
A01 - Variables in R
2.2 - Training Regression Models
2.1 - Introduction to Supervised Learning
2.1 - Introduction to Supervised Learning
1.1 - Introduction to Statistical Learning
1.1 - Introduction to Statistical Learning
Test Document
Network Analysis