During this course, we will continue to learn another important application in supervised learning - solving classification problems. In the following lessons, you will be exposed to: logistic regression, K-nearest neighbor algorithm, naive Bayes, support vector machine, perceptron and artificial neural network, decision tree and random forest, and bagging and boosting methods. The course will start with the principle of each of these methods. You are supposed to fully understand the implementation process of the core algorithm, and then learn to use scikit-learn to construct your own models. The course content of this week is much larger than that of the first week. We hope you can devote due attention to the lessons hereof.