Hands-on Approaches to Handling Data Imbalance | CodeSignal Learn

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Hands-on Approaches to Handling Data Imbalance

Artificial Intelligence

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3 courses

47 practices

6 hours

Master techniques for handling data imbalance in machine learning. Progress from data preparation and baseline modeling to advanced resampling, evaluation metrics, and specialized algorithms for imbalanced datasets to build robust, fair models.

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Certificate of Achievement

Verified skills you'll gain

INTERMEDIATE

Coding and Data Algorithms

DEVELOPING

Machine Learning Model Development

INTERMEDIATE

Model Validation and Selection

Tools you'll use

Pandas

Python

Scikit-learn

Course 1

Preparing Your Data and Setting a BaseLine

4 lessons

15 practices

In this course, you'll go through the essential steps for loading, exploring, and preprocessing the data. You'll handle missing values, encode categorical variables, and train a baseline model to establish a performance benchmark.

Course 2

Handling Unbalanced Datasets

4 lessons

14 practices

In this course, you'll learn to recognize and address class imbalance in datasets. Explore practical undersampling and oversampling techniques, visualize their effects, and apply advanced resampling strategies. By the end, you'll be able to train models that perform better on imbalanced data.

Course 3

Evaluation Metrics & Advanced Techniques for Imbalanced Data

4 lessons

18 practices

This course focuses on evaluating models with imbalanced data, and explores advanced techniques including cost-sensitive learning, ensemble methods, and anomaly detection for extreme imbalance. You'll learn appropriate evaluation strategies specifically designed for imbalanced datasets, helping you choose the right metrics and avoid common pitfalls.

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