Feature Engineering Techniques for Machine Learning

Skillsoft issued completion badges are earned based on viewing the percentage required or receiving a passing score when assessment is required. Feature engineering is about thoughtfully selecting and creating features to boost the accuracy and reliability of machine learning (ML) models. In this course, learn how to select and create features that improve ML model accuracy and reliability, strategies to mitigate overfitting risks, the impact of well-chosen features on model performance, and how to craft new features to enhance predictive capabilities. Next, discover how to build scikit-learn pipelines, implement feature engineering techniques, and how creating polynomial features for regression models helps capture complex data patterns. Finally, explore how to apply log and power transformations and implement principal component analysis (PCA) for dimensionality reduction. After completing this course, you will be able to apply feature engineering techniques for machine learning.