Feature Selection and Dimensionality Reduction

Feature selection and dimensionality reduction are key techniques in machine learning (ML) that streamline data complexity and enhance model performance.In this course, learn various feature selection methods for ML data, the approaches and advantages of these methods, and the importance of dimensionality reduction. Next, examine multicollinearity in your data, implement feature selection using the variance threshold and the f-statistic techniques, use mutual information regression to select features, and explore model-based feature selection using ridge regression for multicollinearity. Finally, explore sequential feature selection, perform feature selection for classification tasks using chi-squared and the f-statistic, and execute recursive feature selection.After completing this course, you will be able to execute feature selection and dimensionality reduction.