SKILLSHARE ARTIFICIAL INTELLIGENCE ACADEMY 3 POLYNOMIAL REGRESSION ANALYSIS-iLLiTERATE

Polynomial Regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable y is modelled as an nth degree polynomial in X. Polynomial regression fits a nonlinear relationship between the value of X and the corresponding conditional mean of Y. denoted E(y |x), and has been used to describe nonlinear phenomena such as the growth rate of tissues, the distribution of carbon isotopes in lake sediments, and the progression of disease epidemics. Although polynomial regression fits a nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown parameters that are estimated from the data. For this reason, Polynomial Regression is considered to be a special case of multiple linear regression.

The predictors resulting from the polynomial expansion of the "baseline" predictors are known as interaction features. Such predictors/features are also used in classification settings.

In this Course you learn Polynomial Regression & Logistic Regression You learn how to estimate output of nonlinear system by Polynomial Regressions to find the possible future output Next you go further You will learn how to classify output of model by using Logistic Regression

In the first section you learn how to use python to estimate output of your system. In this section you can estimate output of:

- Nonlinear Sine Function
- Python Dataset
- Temperature and CO2