1.
Machine Learning Models
1.1.
Linear Regression
1.1.1.
Loss
1.1.2.
Gradient Descent
1.1.3.
Hyperparameters
1.2.
Logistic regression
1.2.1.
Calculating a probability
1.2.2.
Loss and regularization
1.3.
Classification
1.3.1.
Thresholds and the confusion matrix
1.3.2.
Accuracy, recall, precision, and related metrics
1.3.3.
ROC and AUC
1.3.4.
Prediction bias
1.3.5.
Multi-class classification
2.
Data
2.1.
Working with numerical data
2.1.1.
Numerical data: How a model ingests data using feature vectors
2.1.2.
First steps
2.1.3.
Normalization
2.1.4.
Binning
2.1.5.
Scrubbing
2.1.6.
Qualities of good numerical features
2.1.7.
Polynomial transforms
2.2.
Working with categorical data
2.2.1.
Vocabulary and one-hot encoding
2.2.2.
Common issues
2.2.3.
Feature crosses
2.3.
Datasets, generalization, and overfitting
2.3.1.
Data charateristics
2.3.2.
Labels
2.3.3.
Imbalanced Datasets
2.3.4.
Dividing the original dataset
2.3.5.
Transforming data
2.3.6.
Generalization
2.3.7.
Overfitting
2.3.8.
Model complexity
2.3.9.
L2 regularization
2.3.10.
Interpreting Loss curves
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