Basics Ipynb Colab Google Colab

Leo Migdal

-Nov 17, 2025, 3:38 PM

If you want to create a machine learning model but say you don't have a computer that can take the workload, Google Colab is the platform for you. In this article, we'll learn how to use google colab. Google Colab, short for Colaboratory, is a free cloud-based platform provided by Google that allows users to write and execute Python code collaboratively in a Jupyter Notebook environment. Google Collaboratory notebook, is designed to facilitate machine learning (ML) and data science tasks by providing a virtual environment, Google colab python with access to free GPU resources. Google Colab offers several benefits that make it a popular choice among data scientists, researchers, and machine learning practitioners. Key features of Google Collaboratory notebook include:

To start working with Google Collaboratory Notebook you first need to log in to your Google account, then go to this link https://colab.research.google.com// . On opening the website you will see a pop-up containing the following tabs - Welcome to this beginner-friendly repository! 👋 This repository contains a Google Colab Notebook that covers the fundamentals of Python programming along with some basic Data Structures and Algorithms (DSA) concepts. It is designed to help absolute beginners quickly grasp the core concepts of Python and begin their journey into programming and problem-solving. The notebook is divided into clear, easy-to-follow sections with examples, explanations, and code snippets for each topic:

What is Python? Brief overview of Python and why it's popular. Variables and Datatypes Explanation of variables and different datatypes like int, float, string, boolean, etc. An introduction to commonly used data structures and simple algorithms to build a strong foundation for problem-solving. Here you'll find a series of instructive and educational notebooks organized by topic areas. This notebook contains an example of using the Gemini API to analyze a a product sketch (in this case, a drawing of a Jet Backpack), create a marketing campaign for it, and output taglines...

This notebook provides an example of how to prompt Gemini 1.5 Pro using an audio file. In this case, you'll use a sound recording of President John F. Kennedy’s 1961 State of the Union address. System instructions allow you to steer the behavior of the model. By setting the system instruction, you are giving the model additional context to understand the task, provide more customized responses, and adhere to guidelines over the user interaction. Using function calling allows you to control how the Gemini API acts when tools have been specified.

Google Colab is a product from Google Research that allows anyone to write and execute arbitrary Python code through the browser. It's especially well-suited for machine learning, data analysis, and educational purposes. With Colab, you can leverage the power of GPUs and TPUs for free, making it a popular choice for resource-intensive tasks. When running deep-learning scripts, you will need to change from CPU to GPU. To do this, click Runtime → Change runtime type, then select T4 GPU. Then click Save.

Note: You are encouraged to sign up to Google Colab with an existing Google account. If you create a new Google account, your GPU usage may be cut off. You may find yourself doing a tutorial where the creator provides you with a link to a Google Colab link. Just like in our Fine-Tuning Embedding Models Course, we provided a link to this Google Colab book. To run external Colab notebooks, you run them as you would any notebook you’ve built yourself. Just click the run icon that’s located to the left of the cell and it will execute the code.

It’s important to run each cell in succession. Python is a versatile programming language widely used in various fields such as web development, data analysis, artificial intelligence, scientific computing, and more. This article explains how to perform multiple programming tasks using Google Colab, a popular cloud-based platform for coding. Python is a high-level programming language. It was first released in 1991. It became popular due to clear syntax and simpler coding processes compared to C++ and Java.

It is preferred over other coding applications because of: There are several libraries and environments available for Python development, each catering to different needs. Google provides a Jupyter Notebook-type interface for running Python code on an online virtual machine such as Google Colab. Google Colab comes with many popular Python libraries pre-installed, such as numpy, pandas, matplotlib, and tensorflow. Colab allows for seamless collaboration, much like Google Docs. You can share your notebook with others, giving them access to view, comment on, or edit the notebook.

This feature is particularly useful for team projects, code reviews, and peer learning. Colab autosaves work progress in Google Drive. You can also download notebooks in several formats to work offline or integrate them with other tools. Colab allows you to mount your Google Drive, making it easy to access, read, and write files directly. This feature is beneficial for loading datasets, saving processed data, or storing outputs and models. Now you can start writing your code and saving it to your Google Drive.

Colab is a hosted Jupyter Notebook service that requires no setup to use and provides free access to computing resources, including GPUs and TPUs. Colab is especially well suited to machine learning, data science, and education. Check out our catalog of sample notebooks illustrating the power and flexiblity of Colab. Read about product updates, feature additions, bug fixes and other release details. Check out these resources to learn more about Colab and its ever-expanding ecosystem. We’re working to develop artificial intelligence responsibly in order to benefit people and society.

Basics Ipynb Colab Google Colab

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