Supervised Learning
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Model Selection techniques - AIC, BIC, Mallow's Cp , Adjusted R-squared , Cross validation error.
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Shrinkage Methods and Regularization techniques - Ridge Regression , LASSO, L1 norm, L2 norm.
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Non-linear Regression and parametric models like polynomial regression, splines
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Non-parametric model - K-nearest neighbor algorithm
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Tree based Modelling - Decision Trees
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Ensemble learning - Boosting , Bagging.
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Re-sampling methods and Cross Validation
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Unsupervised learning like kmeans, pca