With Colab you can harness the full power of popular Python libraries to analyze and visualize data. The code cell below uses numpy to generate some random data, and uses matplotlib to …

In this practical, we will learn about the programming language Python as well as NumPy and Matplotlib, two fundamental tools for data science and machine learning in Python.

Colab provides automatic completions to explore attributes of Python objects, as well as to quickly view documentation strings. As an example, first run the following cell to import the numpy …

To run the following cell, your API key must be stored it in a Colab Secret named GOOGLE_API_KEY. If you don't already have an API key, or you're not sure how to create a …

The new Google Gen AI SDK provides a unified interface to Gemini models through both the Gemini Developer API and the Gemini API on Vertex AI. With a few exceptions, code that runs …

Introducing our new google.colab.ai library! Pro & Pro+ subscribers can access powerful Gemini and Gemma models with just a few lines of code- no set up or API key required!

Title Overview of Colab Features Markdown Guide Charts in Colab External data: Drive, Sheets, and Cloud Storage Getting started with BigQuery Forms

This tutorial is designed to run as a Python notebook on Colab. We’ll take a closer look at Colab and its features in a separate tutorial, but for now, here is what you need to know: When you …

In this practical, we will learn about the programming language Python as well as NumPy and Matplotlib, two fundamental tools for data science and machine learning in Python.

This section will describe four primary ways you can run Python code: the Python interpreter, the IPython interpreter, via Self-contained Scripts, or in the Jupyter notebook.