In this tutorial we will predict home prices using linear regression. We use training data that has home areas in square feet and corresponding prices and train a linear regression model using sklearn linear regression class. Later on predict method is used on linear regression object to make actual forecast. Exercise CSV file is here: https://github.com/codebasics/py/tree... Code in this tutorial is here: https://github.com/codebasics/py/tree... (check the .ipynb file) To download csv and code for all tutorials: go to https://github.com/codebasics/py, click on a green button to clone or download the entire repository and then go to relevant folder to get access to that specific file. #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #LinearRegression #sklearntutorials #scikitlearntutorials Topics that are covered in this Machine Learning Video: 0:00 Simple linear regression 1:59 Linear equation 2:22 Import data in dataframe 2:43 Import sklearn library 3:52 Plot scatter plot 5:26 Create Linear Regression object 13:35 Exercise at the end to predict canada's per capita income Topic Highlights: 1) What is linear regression 2) Mean squared error 3) Predict home prices by minimizing mean squared error (or MSE) 4) Exercise at the end to predict canada's per capita income