Page 25 - E-catelog for advanced Course (2)

 Linear Regression

 Scatter Diagram

 Correlation Analysis

 Principles of Regression

 Introduction to Simple Linear Regression

 R shiny and Python Flask

 Introduction to R shiny and Python Flask (deployment)

 Multiple Linear Regression

 Scatter diagram

 Correlation Analysis

 Correlation coefficient

 Ordinary least squares

 Principles of regression

 Splitting the data into training, validation and testing datasets

 Understanding Overfitting (Variance) vs Underfitting (Bias)

 Generalization error and Regularization techniques

 Introduction to Simple Linear Regression

 Heteroscedasticity / Equal Variance

 LINE assumption

 Collinearity (Variance Inflation Factor)