to

### Machine Learning Basics: Polynomial Regression

Learn to build a Polynomial Regression model to predict the values for a non-linear dataset. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

### Machine Learning Basics: Polynomial Regression

In previous stories, I have given a brief of Linear Regression and showed how to perform Simple and Multiple Linear Regression. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

### Machine Learning & Linear Regression

This course is targeted for Beginner Python Developers who want to kickstart their journey in Machine Learning. In this course, we are going to use a linear regression model from scikit-learn library in Python to predict the total no. of positive cases for COVID19 in a particular state in India. After completing this course, you'll be able to:

### Is robust regression less sensitive towards outlier and how?

In case of a standard linear regression the coefficients are computed by minimizing the sum of residuals. Here in robust regression a weight is multiplied with each residual depending on the weightage given to each point( sum(weight*residuals)). So we can assign low weights for outliers and high weights for influential points by defining some weight function accordingly. If you look closely standard linear regression is a special case of robust regression where weight of any point is 1.

### Learn the Concept of linearity in Regression Models

This Tutorial talks about basics of Linear regression by discussing in depth about the concept of Linearity and Which type of linearity is desirable. Linear regression however always means linearity in parameters, irrespective of linearity in explanatory variables. Here the variable X can be non linear i.e X or X² and still we can consider this as a linear regression. However if our parameters are not linear i.e say the regression equation is A function Y f(x) is said to be linear in X if X appears with a power or index of 1 only. Y is linearly related to X if the rate of change of Y with respect to X (dY/dX) is independent of the value of X. B2 is Linear but B1 is non-linear but if we transform?