Statistical-Learning-using-R

Topics Covered :

Supervised Learning

1. Model Selection techniques - AIC, BIC, Mallow's Cp , Adjusted R-squared , Cross validation error.

2. Shrinkage Methods and Regularization techniques - Ridge Regression , LASSO, L1 norm, L2 norm.

3. Non-linear Regression and parametric models like polynomial regression, splines

4. Non-parametric model - K-nearest neighbor algorithm

5. Tree based Modelling - Decision Trees

6. Ensemble learning - Boosting , Bagging.

7. Re-sampling methods and Cross Validation

8. Unsupervised learning like kmeans, pca