How to use Kaggle Notebooks

2026-04-03 · Source: Kaggle · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Novice, short

Summary

This guide introduces participants to Kaggle Notebooks for a 5-day AI agents intensive course with Google. It covers essential steps for beginners, including how to copy and edit notebooks to enable hands-on code execution. The guide explains notebook structure, differentiating between markdown and code cells, and emphasizes the correct sequential execution of code cells, particularly for import statements. A critical section details the secure handling of the Gemini API key, strongly advising against pasting it directly into notebooks due to sharing risks. Instead, it demonstrates adding the API key as a Kaggle secret and then securely importing it into the notebook environment, ensuring privacy and proper agent functionality.

Key takeaway

For AI Students and AI Engineers participating in the Google AI agents course, you should prioritize setting up your Kaggle Notebooks correctly. Always add your Gemini API key as a Kaggle secret rather than pasting it directly into a notebook cell. This practice prevents accidental exposure of your key if you share your work, safeguarding your credentials and ensuring secure development.

Key insights

Securely manage API keys in Kaggle Notebooks by using secrets, not plain text.

Principles

• Execute notebook cells sequentially.
• Prioritize API key security.

Method

To secure API keys: add key via Kaggle "Add-ons" > "Secrets" menu, label it "Google API_key", save, and ensure the checkbox is ticked. Then, import it via code.

In practice

• Copy and edit notebooks for active participation.
• Run import statements before dependent code.
• Use Kaggle secrets for API key management.

Topics

• Kaggle Notebooks
• AI Agents Course
• Google Gemini API
• API Key Management
• Kaggle Secrets

Best for: AI Student, AI Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Kaggle.