Summary
In this chapter, we learned what Kaggle Notebooks are, what types we can use, and with what programming languages. We also learned how to create, run, and update notebooks. We then visited some of the basic features for using notebooks, which will allow you to start using notebooks in an effective way, to ingest and analyze data from datasets or competitions, to start training models, and to prepare submissions for competitions. Additionally, we also reviewed some of the advanced features and even introduced the use of the Kaggle API to further extend your usage of notebooks, allowing you to build external data and ML pipelines that integrate with your Kaggle environment.
The more advanced features give you more flexibility in using Kaggle Notebooks. With Utility scripts, you can create modular code, with specialized Python modules for ingesting data, performing statistical analysis on it, preparing visualizations, generating features, and building models. You can reuse...
