# Introduction In supervised learning, we want to learn the relationship between two datasets: the observed data `X` and an external variable `y` that we want to predict. There are two main types of supervised learning problems: classification and regression. In classification, the goal is to predict the class or category of an observation, while in regression, the goal is to predict a continuous target variable. In this lab, we will explore the concepts of supervised learning and see how to implement them using scikit-learn, a popular machine learning library in Python. We will cover topics such as nearest neighbor classification, linear regression, and support vector machines (SVMs). ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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