dealing with real-world data in this we're going to talk about the challenges of dealing with real-world dataand some of the quirks you might run into the starts by talking about the bias-variance trade-offwhich is kind of more principled way of talking about the different ways you might overfit and underfit dataand how it all interrelates with each other we then talk about the -fold cross-validation techniquewhich is an important tool in your chest to combat overfittingand look at how to implement it using python nextwe analyze the importance of cleaning your data and normalizing it before actually applying any algorithms on it we see an example to determine the most popular pages on website which will demonstrate the importance of cleaning data the also covers the importance of remembering to normalize numerical data finallywe look at how to detect outliers and deal with them specificallythis covers the following topicsanalyzing the bias/variance trade-off the concept of -fold cross-validation and its implementation the importance of cleaning and normalizing data an example to determine the popular pages of website normalizing numerical data detecting outliers and dealing with them