Building ML Regression Models using Scikit-Learn
By PARTHA MAJUMDAR, Director - Professional Services
This course is for you
This course is aimed at students and practitioners of Data Sciences for building 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 about 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 learnt 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 them through Excel, and also it can be experienced visually, we start the course through explanation of the associated concepts using Excel. This course requires the Learners to have prior knowledge of Computer Software programming, knowledge of programming using Python and also some knowledge of Predictive Analytics.
Course overview - 10
Understanding Linear Regression
Understanding Linear Regression through Excel
How to measure goodness of a Regression Model?
Understanding Random Forest Algorithm for Regression
Understanding Support Vector Machine (SVM) for Regression
Libraries & Functions in Scikit-Learn required for creatingRegression Models
Building Regression Models using Scikit-Learn