Linear Regression is the most fundamental form of regression and a very common and widely used predictive technique in the field of analytics.

Logistic regression is often used to describe the relationship between a binary predicted variable and a set of predictor variables. The robust technique is often used to describe the relationship between a binary predicted variable and a set of predictor variables.

Through an experiential learning approach, the course will take you through the concepts of 'Simple and Multiple Linear Regression and the various nuances involved in the building of a robust predictive analytical solution. It follows a theoretical and practical approach to provide you with complete knowledge for the evaluation of a regression model.

The course is designed to guide learners through various stages of the predictive modeling framework and will focus not only on providing exposure to building a logistic model but also on interpreting the results of the model as well.

After completion of this course, you will be able to analyze, visualize and build supervised learning models using Linear and Logistic Regression. This would not only give you a good start in your career but also would lay a strong foundation for your journey in Data Science.