Machine Learning with Imbalanced Data Free Download. Learn multiple techniques to tackle data imbalance and improve the performance of your machine learning models.
Table of Contents
Machine Learning with Imbalanced Data Description
Requirements
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Knowledge of machine learning basic algorithms, i.e., regression, decision trees and nearest neighbours
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Python programming, including familiarity with NumPy, Pandas and Scikit-learn
Complete Intro to Linux and the Command-Line – Frontend masters
What you’ll learn
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Under-sampling methods at random
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Under-sampling methods which focus on observations that are harder to classify
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Under-sampling methods that ignore potentially noisy observations
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Over-sampling methods to increase the number of minority observations
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Ways of creating syntethic data to increase the examples of the minority class
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SMOTE and its variants
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Use ensemble methods with sampling techniques to improve model performance
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The most suitable evaluation metrics to use with imbalanced datasets
Who this course is for:
- Data Scientists and Machine Learning engineers working with imbalanced datasets
Machine Learning with Imbalanced Data Free Download
Content From: https://www.udemy.com/course/machine-learning-with-imbalanced-data/
