Things With Stuff

Learn how to create a robust data cleaning and visualization tool using Python, Pandas, SQLite, and Streamlit with the help of AI to simplify and debug your code. This setup can handle large datasets and provide beautiful visualizations, making it great for quick prototyping and sharing data visualizations. With this tutorial, you can learn how to work with unstructured or dirty data, manipulate and analyze data with Pandas, and use Jupyter notebooks for interactive coding. You can also use AI to generate functions or even entire scripts and troubleshoot bugs with ease. Happy coding!

Machine learning algorithms learn by being fed large amounts of data and identifying patterns, helping them make predictions or decisions. Many-shot prompts involve providing numerous examples, while few-shot prompts offer only a few examples. Zero-shot prompts rely solely on pre-trained knowledge. Controlling language model output involves defining constraints such as desired length, format, syntax, and explicit exclusions. Iterative reasoning enhances prompt refinement. Roleplay can enhance language model efficacy by defining persona and flavor. Mastering prompt engineering enhances efficiency in procurement.