This course is aimed at students and practitioners of data sciences to build predictive analytics models for research and commercial purposes.

Machine learning can be used to solve prediction problems for classification and regression. In this course, we discuss using machine learning for building regression models. We will use python language. In python, we have many options for building machine learning solutions like tensor flow, Keras, etc. In this project, we use scikit-learn.

Scikit-learn provides a comprehensive array of tools for building regression models (scikit-learn also has tools for solving classification problems). The concepts learned in this project can be extended to build neural networks and other types of models using tools like Tensor Flow or Keras, etc using Python or any other language like R.

Before diving into building regression models using scikit-learn, the course discusses the concepts required to understand the process and mechanism for building such models. As it is, easy to understand the concepts working through excel. And also it can be experienced visually. We start the course through an explanation of the associated concepts using excel.