# Introduction In this lab, we will explore the concept of semi-supervised learning, which is a type of machine learning where some of the training data is labeled and some is unlabeled. Semi-supervised learning algorithms can leverage the unlabeled data to improve the model's performance and generalize better to new samples. This is particularly useful when we have a small amount of labeled data but a large amount of unlabeled data. In this lab, we will focus on two semi-supervised learning algorithms: Self Training and Label Propagation. We will learn how to implement and use these algorithms using scikit-learn, a popular machine learning library in Python. ## 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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