Many times we get in a dilemma of which machine learning model should we use for a given problem. KFold cross validation allows us to evaluate performance of a model by creating K folds of given dataset. This is better then traditional train_test_split. In this tutorial we will cover basics of cross validation and kfold. We will also look into cross_val_score function of sklearn library which provides convenient way to run cross validation on a model #MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #MachineLearningModel #sklearn #sklearntutorials #scikitlearntutorials Code: https://github.com/codebasics/py/blob... Exercise: Exercise description is avialable in above notebook towards the end Exercise solution: https://github.com/codebasics/py/blob... Topics that are covered in this Video: 0:00 Introduction 0:21 Cross Validation 1:02 Ways to train your model( use all available data for training and test on same dataset) 2:08 Ways to train your model( split available dataset into training and test sets) 3:26 Ways to train your model (k fold cross validation) 4:26 Coding (start) (Use hand written digits dataset for kfold cross validation) 8:23 sklearn.model_selection KFold 9:10 KFold.split method 12:21 StratifiedKFold 19:45 cross_val_score