Feature Engineering for Machine Learning
- Description
- Curriculum
- Reviews
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1
1. Introduction to Feature engineeringThis lesson introduces feature engineering concepts and explains how transforming raw data into meaningful inputs improves machine learning model performance and prediction accuracy.
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2. Concept of undersampling and oversampling
This lesson explains techniques used to handle imbalanced datasets through undersampling and oversampling methods to improve machine learning model fairness and performance.
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3. Hands-on exercise showing tuning of model
This lesson provides a practical demonstration of model tuning techniques used to optimize machine learning performance through parameter adjustments and evaluation methods.
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