In this video, I'll be talking about how to avoid overfitting the models in machine learning. If you don't know what overfitting is, check the link in the comment section below. Strategy number one, data cleaning. It's as simple as that. Garbage and garbage out. Machine learning models work this way. If you are not cleaning your data, the model is going to learn the garbage and the data, and it's going to throw the garbage out. Strategy number two, don't use all the available data for training purposes. Split it into train, validation, and test set. Use the training for the training, validation for the tuning, and test for devaluation. Strategy number three, cross validation. Shuffle the data, split it, train it, evaluate it. Shuffle it again, split it, evaluate it. Do it 10 different times. Evaluate 10 different times. Every result is out. It's going to improve the precision of the model. Strategy number four, sometimes you don't have enough data available for training, a model. Gather more data.