Nonlinear Regression
This course covers a supervised regression technique called Non Linear regression which is used to model a relationship between a certain number of features and a continuous target variable. Students will learn how this relationship is then used to predict changes in the target variable. The course includes the background, how to build, evaluate and interpret these Non Linear Regression models.
4 hours of instruction
This course covers a supervised regression technique called Non Linear regression which is used to model a relationship between a certain number of features and a continuous target variable. Students will learn how this relationship is then used to predict changes in the target variable. The course includes the background, how to build, evaluate and interpret these Non Linear Regression models.
OBJECTIVES
- Identify the working and use cases of nonlinear regression models
- Build, optimizing and evaluate these nonlinear regression models
- Assess statistical significance and validate models for explanatory power and bias
PREREQUISITES
Multiple Linear Regression in Python
SYLLABUS & TOPICS COVERED
- Nonlinear
- Optimizing and evaluating nonlinear regression models
- Understanding how to model interactions
SOFTWARE REQUIREMENTS
You will have access to a Python-based JupyterHub environment for this course. No additional download or installation is required.
About Instructor
Not Enrolled
Login
Accessing this course requires a login. Please enter your credentials below!
