[ FreeCourseWeb.com ] Machine Learning with Imbalanced Data



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MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch

Genre: eLearning | Language: English + .srt | Duration: 75 lectures (5h 27m) | Size: 1.57 GB

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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