# Introduction In this lab, we will learn how to use Support Vector Machines (SVM) with Stochastic Gradient Descent (SGD) to classify data. SVM is a powerful classification algorithm that is widely used in machine learning for classification and regression analysis. The idea behind SVM is to find the best hyperplane that separates the data into classes with the largest possible margin. The margin is the distance between the hyperplane and the closest data points from each class. Stochastic Gradient Descent (SGD) is an optimization algorithm that is used to find the best parameters for the SVM algorithm. ## 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.
Click the virtual machine below to start practicing