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Machine Learning: Algorithms & Applications
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Module 4
Regularized Linear Models
1
Ridge and Lasso Regularization: Preventing Overfitting and Feature Selection
Explain Ridge (L2) and Lasso (L1) regularization, including how each addresses overfitting and Lasso's feature selection property.
2
Elastic Net: Blending Ridge and Lasso Regularization
Explain Elastic Net as a combination of Ridge and Lasso and when to prefer each regularization approach.
3
Ridge and Lasso Regression: Regularization in Practice
Apply Ridge and Lasso regression to a dataset and interpret the impact of regularization on model coefficients.
4
Comparing Regularized Regression Models
Apply Elastic Net and compare its performance against Ridge, Lasso, and standard linear regression.
Previous module
Linear Regression and Core Concepts
Next module
Logistic Regression and Classification Evaluation