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Fintech ML & Data Engineering
·
Module 1
Classical ML Foundations
1
Understanding Bias-Variance Trade-off
Explain the bias-variance tradeoff and its implications for model complexity.
2
L1 vs. L2 Regularization: Impact on Model Parameters
Compare L1 (Lasso) and L2 (Ridge) regularization and their effects on model parameters.
3
Understanding Logistic Regression: Probability and Decision Boundaries
Describe how logistic regression models probability and produces a decision boundary.
4
Decision Trees, Bagging, and Boosting
Explain decision trees, bagging (Random Forest), and boosting (Gradient Boosting).
5
SVM and the Kernel Trick
Explain the SVM algorithm, including the role of the kernel trick for non-linear classification.
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Feature Engineering and Preprocessing