# Introduction In this lab, we will explore the preprocessing techniques available in scikit-learn. Preprocessing is an essential step in any machine learning workflow as it helps to transform raw data into a suitable format for the learning algorithm. We will cover various preprocessing techniques such as standardization, scaling, normalization, encoding categorical features, imputing missing values, generating polynomial features, and creating custom transformers. ## 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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