Certificate In Data Science: Machine Learning
- Description
- Curriculum
- Reviews
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1
Introduction -
2Notation
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3Example Zip Code Reader
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4Evaluation metrics
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5Confusion matrix
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6Sensitivity, specificity, and prevalence
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7Balanced accuracy and F1 score
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8Prevalence matters in practice
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9ROC and precision-recall curves
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10Loss Function
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11Conditional probabilities
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12Conditional expectations
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13Linear Regression for Prediction Case Study 2 or
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14Smoothing
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15Bin Smoothing
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16Local Weighted Regression (loess)
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17Beware of Default Smoothing Parameters
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18Connecting Smoothing to Machine Learning
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19k-Nearest Neighbors (kNN)
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20Over training and Over smoothing
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21Choosing k
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22Mathematical description of cross-validation
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23k-fold cross-validation
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24Caret Package
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25Fitting with Loess
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26MNIST case study preprocessing
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27MNIST case study kNN
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