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Machine Learning Syllabus Overview
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Module 4
Linear Regression and Regularization
1
Least Squares Regression
Formulate linear regression as a model that minimizes the sum of squared errors.
2
Linear Regression with Scikit-learn: Fitting and Interpretation
Fit a linear regression model using scikit-learn and interpret its coefficients.
3
Bias-Variance Trade-off
Explain the concepts of bias and variance and their trade-off in model complexity.
Understanding Overfitting and Regularization
Define overfitting and explain how regularization techniques are used to mitigate it.
Ridge vs. LASSO: Regularization and Feature Selection
Contrast L2 (Ridge) and L1 (LASSO) penalties, highlighting LASSO's feature selection property.
6
Regularized Regression in Scikit-learn
Apply Ridge and LASSO regression using scikit-learn and observe the effect on model coefficients.
Cross-Validation for Optimal Regularization
Use cross-validation to select the optimal regularization strength (alpha).
Previous module
Clustering with K-Means and GMMs
Next module
Instance-Based and Probabilistic Classification