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TORRENT DETAILS
Udemy - Machine Learning With Imbalanced Data
TORRENT SUMMARY
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Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models.
What you'll learn:
Under-sampling methods at random
Under-sampling methods which focus on observations that are harder to classify
Under-sampling methods that ignore potentially noisy observations
Over-sampling methods to increase the number of minority observations
Ways of creating syntethic data to increase the examples of the minority class
SMOTE and its variants
Use ensemble methods with sampling techniques to improve model performance
The most suitable evaluation metrics to use with 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
Description
Welcome to Machine Learning with Imbalanced Datasets. In this course, you will learn multiple techniques which you can use with imbalanced datasets to improve the performance of your machine learning models.
If you are working with imbalanced datasets right now and want to improve the performance of your models, or you simply want to learn more about how to tackle data imbalance, this course will show you how.
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