How to debug a Jupyter notebook from Kaggle with LLMs & AI ?

In this hands-on course, you will learn how to harness the power of Generative AI and Large Language Models (LLMs) to debug and enhance a real-world Jupyter notebook from Kaggle. The chosen use case focuses on Text Mining and Naïve Bayes Classification, providing a rich opportunity to explore Natural Language Processing (NLP) techniques in a practical context.

You’ll begin by reviewing a public Kaggle notebook that performs sentiment analysis or topic classification using a Naïve Bayes model. Then, with the help of state-of-the-art LLMs (like ChatGPT or similar tools), you’ll identify errors, inefficiencies, and opportunities for improvement in the notebook. This includes fixing bugs, optimizing code, improving data preprocessing steps, and refining model evaluation.

The course demonstrates how AI can act as a collaborative assistant, guiding you through debugging and enhancement while teaching core concepts in machine learning and text analytics. You’ll also gain valuable experience in working with Python, scikit-learn, Pandas, and NLTK or similar NLP libraries.

Whether you’re a data science beginner or a practitioner curious about integrating LLMs into your workflow, this course offers an exciting blend of AI-assisted development, critical thinking, and hands-on machine learning.

Unlock the future of coding with AI—join us today! So Let’s Go !