Linear and Non-Linear Regression, Lasso Ridge Regression, SHAP, LIME, Yellowbrick, Feature Selection | Outliers Removal
InstructorKGP Talkie | Laxmi Kant
Instruction covers sophisticated model explanation techniques using SHAP and LIME, alongside optimization methods like Recursive Feature Elimination and regularization via Ridge and Lasso.
The curriculum emphasizes statistical rigor by covering model assumptions, including homoscedasticity, multicollinearity, and various mathematical transformations for non-normality.
The reliance on the Boston dataset presents a hurdle, as it has been removed from recent scikit-learn versions, requiring manual data loading to complete the lessons. Note that this signal was recorded before the displayed update date; while an update is noted, it does not prove these specific issues were corrected.
One sampled review suggests that the learning path lacks integrated coding exercises or multiple-choice quizzes to reinforce concepts before moving into larger segments. This signal also predates the displayed update, and the update label does not guarantee a correction of this limitation.
The progression from Python basics to machine learning aligns well with the stated beginner target audience.