Linear Regression Machine Learning Algorithm From Scratch in Python is a short video course to discuss an overview of the Linear Regression Machine Learning Algorithm. In this video different types of concepts related to Linear Regression Algorithms such as Mean, Variance, and Covariance are discussed. Finally, the algorithm is implemented, step by step, in Python…
In this video, I will be discussing, how to Implement a Linear regression machine learning algorithm, in 10 Lines. After completing this tutorial, you will not only learn about, linear regression algorithms, but also, you will be able to train the model, using the Jupyter notebook. Happy Learning !!
Decision Tree can be used for implementing regression as well as classification models, however , Linear Regression can be used for regression problem only. Decision tree can be used for regression, even when there is non linear relationships between the features and the output variable. However, Linear Regression works really nicely when there is linear relationship…
Root mean squared error or RMSE is a measure of the difference between actual values and predicted values of a machine learning model like Linear Regression. Root mean squared error is a measure of how well the machine learning model can perform. The lower the RMSE, the better the model. RMSE is always positive, and…
For Linear Regression, R-squared is a statistical term which indicates how close the data are to the fitted regression line. R-Squared is also known as coefficient of determination. R-squared = Explained variation in data / Total variation in data R-squared = 1 – (RSS/TSS) RSS = Sum of squares of difference between predicted value and…
We can not implement Linear Regression on any given data. If we have got a dataset and we are planning to implement Linear Regression Model on that data we must first check if the given data is inline with the assumptions of Linear Regression. Once the given data agrees to all the assumptions then only…
Linear regression is one of the most popular and fundamental machine learning algorithm. If relationship between two variables are linear we can use Linear regression to predict one variable given that other is known. For example if we are researching how the price of the house will vary if we change the area of the…
