Welcome back. This second course of this specialization is focused on medical prognosis. Prognosis is a branch of medicine that specializes in predicting the future health of patients. For example, given a patient's lab results, can you estimate their risk of having a heart attack over the next five years or the risk of dying over the next 10 years? In this course, you also get to practice building decision trees and random forests using structured data. Machine learning is a powerful tool for prognosis and can provide a tremendous boost to this branch of medicine by using many different types of medical data to make accurate predictions about a patient's future health. In this first week, you'll learn what is prognosis and we'll see multiple examples of prognostic tasks, including a few examples where prognosis using risk calculations is part of routine clinical practice. In the second week, you will build machine learning models with decision trees. You will use trees to model non-linear relationships, which are commonly observed in medical data, and apply them to the prognostic task of predicting mortality, that is the chance of a patient dying. In practice, when we train machine learning models, one of the key challenges is how to handle missing data. You'll learn about a few ways of dealing with missing data in your machine learning pipelines. In week three, you learn about survival models. Say a patient has a particular type of cancer and you'd like to estimate the probability of their surviving one year or two years or five years or even longer. This is when you use a survival model. It allows you to model the time to an event, in this case, the patient's possible death. These models help doctors answer patient questions like, how likely am I to survive the next five years or the next ten years? Survival models are also used to model the time from treatment to recurrence. So questions like, how likely am I to get a recurrence of this cancer in one year or in two years? In week four, you will learn about strategies to build and evaluate survival models that allow you to compare the risk of individual patients. You will learn about two such models, the Cox Proportional Hazards Model and Survival Trees. Finally, you'll learn about a way to evaluate the prediction performance of the survival prediction models that you built. As we collect larger and larger medical data sets, machine learning will become an invaluable tool to learn the complex relationships in medical data and to help us answer questions like, why some people survive longer than others? Or what is the patient's 10-year risk of heart attack? Let's dive in.