Machine Learning with Imbalanced Data For Free

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Ruchika oberoi

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Mar 27, 2022
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[Download] Machine Learning with Imbalanced Data For Free

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What you’ll learn

  • Apply random under-sampling to remove observations from majority classes
  • Perform under-sampling by removing observations that are hard to classify
  • Carry out under-sampling by retaining observations at the boundary of class separation
  • Apply random over-sampling to augment the minority class
  • Create syntethic data to increase the examples of the minority class
  • Implement SMOTE and its variants to synthetically generate data
  • Use ensemble methods with sampling techniques to improve model performance
  • Change the miss-classification cost optimized by the models to accomodate minority classes
  • Determine model performance with the most suitable metrics for imbalanced datasets

Requirements

  • Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
  • Python programming, including familiarity with NumPy, Pandas and Scikit-learn
  • A Python and Jupyter notebook installation

Who this course is for:

  • Data scientists and machine learning engineers working with imbalanced datasets
  • Data scientists who want to improve the performance of models trained on imbalanced datasets
  • Students who want to learn intermediate content on machine learning
  • Students working with imbalanced multi-class targets


RAR password: [email protected]