# Introduction In this lab, we will learn about K-Means++ initialization using the scikit-learn library in Python. K-Means++ is a popular algorithm for clustering data into groups based on similarities. It is used as the default initialization for k-means. In this lab, we will generate sample data, calculate seeds from k-means++, and plot the init seeds alongside the sample data. ## 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