Python Machine Learning - Code Examples

• Dealing with missing data
  • Identifying missing values in tabular data
  • Eliminating training examples or features with missing values
  • Imputing missing values
  • Understanding the scikit-learn estimator API
• Handling categorical data
  • Nominal and ordinal features
  • Creating an example dataset
  • Mapping ordinal features
  • Encoding class labels
  • Performing one-hot encoding on nominal features
• Partitioning a dataset into separate training and test sets
• Bringing features onto the same scale
• Selecting meaningful features
  • L1 and L2 regularization as penalties against model complexity
  • A geometric interpretation of L2 regularization
  • Sparse solutions with L1 regularization
  • Sequential feature selection algorithms
• Assessing feature importance with random forests
• Summary

Please refer to the README.md file in ../ch01 for more information about running the code examples.