Regression models are supervised machine learning techniques used to predict continuous numerical values. By analyzing relationships between independent variables (features) and a dependent variable (target), they identify trends to forecast future outcomes. Statistical machine learning combines traditional statistical inference with computational algorithms to learn patterns from data, quantify uncertainty, and make predictions. This course provides an in depth and comprehensive coverage on Multiple Linear Regression models and Logistic Regression and focusses on complete breadth and depth of statistical measures that play a pivot role in carrying out the regression analysis.
The course provides a detailed stepwise understanding beginning with concepts on correlation, all variants of R-Square, least squares line fit, concept of regression and gradually introduces the student to a complete understanding on Simple linear regression models and finally leading the student to a comprehensive understanding on Multiple linear and logistic regression models. This course also ensures that the student understand transformations on non-linear relationships between the predictor and the response variables where necessary so that even non-linear relationships are effectively handled and accommodated within linear models
