How To Use Google Colab 02 Machine Learning For Beginners

Leo Migdal

-Nov 17, 2025, 4:20 AM

how to use google colab 02 machine learning for beginners

The Google Colab is a cloud-based Jypyter notebook platform that can be used in Data Science. The colab platform is freely accessible to everyone and it auto-saves the projects. This allows us to run and train complex machine-learning models efficiently. It provides a user-interactive development environment, GPU(Graphical Processing Unit) and TPU(Tensor Processing Unit) access, storage on Google Drive, real-time collaboration, and numerous other advanced features. The GPU is designed for parallel processing which means it can support multitasking. Thus, this allows the distribution of training processes and helps maintain the speed of machine learning operations.

In this article, we will discuss How to use Google Colab for Machine Learning Projects and also How to start Start Machine Learning Project in Colab. You can open the colab by clicking the following step is as follows - Go to Files -> See the upload icon -> upload the file from your desktop files. Let’s implement the project of Gold Price Prediction using Machine Learning in a colab platform- 8:18 am October 27, 2025 By Julian Horsey Have you ever wondered how some of the most complex AI models or data-driven insights are built without requiring a supercomputer or expensive software?

Enter Google Colab, a platform that has become a fantastic option for coders, data scientists, and curious beginners alike. Imagine writing and running Python code, training deep learning models, or visualizing data, all from your browser, and for free. With its cloud-based infrastructure and seamless integration with tools like GitHub and TensorFlow, Google Colab has provide widespread access tod access to powerful computational resources. Whether you’re just starting out or pushing the boundaries of AI research, this platform offers a unique blend of simplicity and sophistication that’s hard to match. In this beginner-friendly breakdown, David Ondrej explains how Google Colab can transform the way you approach coding and data science. From its free GPU and TPU access to its interactive coding environment, we’ll explore the tools and features that make it a favorite among professionals and hobbyists alike.

You’ll also learn how to avoid common pitfalls, such as session timeouts and execution errors, while mastering tips to enhance your productivity. By the end, you’ll not only understand why Google Colab is a go-to platform in 2026 but also feel equipped to harness its potential for your own projects. After all, the future of coding isn’t just about writing better code, it’s about working smarter, not harder. Google Colab is equipped with a range of features that cater to users across skill levels. These features are designed to simplify workflows and enhance productivity: These capabilities allow users to focus on solving problems and experimenting with ideas, rather than being bogged down by hardware or software limitations.

If you’re new to Python, data science, or deep learning, Google Colab is one of the easiest places to start. It’s like a notebook you can use in your browser without installing anything on your computer — and it even gives you free access to GPUs for deep learning. Google Colaboratory (or Colab) is a free Jupyter Notebook environment provided by Google. You can: You don’t need to install Python, Jupyter, or anything else. Using your Google account, you can simply go to https://colab.research.google.com and start coding!

When working in Colab, your files are not on your computer — they’re in the cloud. So if you want to save your work or load a dataset, it’s best to connect your Google Drive. It will open a link and ask you to sign in and give permission. After that, your Google Drive becomes available under /content/drive/MyDrive/. 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 powerful language for data analysis and machine learning, and Google Colab is an excellent tool for Python machine learning projects.

In this tutorial, we’ll go over how to use Google Colab for Python machine learning projects. Google Colab is a free online platform that allows you to run Jupyter notebooks on Google’s servers. To get started, you will need a Google account. Once you’re logged in, you can create a new notebook by going to File > New notebook. To start our Python machine learning project in Google Colab, we need to import the necessary libraries. We can use the following code to import common Python machine learning libraries:

Before building our machine learning model, we need to preprocess the data. In this tutorial, we will use the famous Iris dataset for classification. We can use the following code to load the Iris dataset: Next, we can split the data into training and testing sets using the following code: Google Colab has become an essential tool for data scientists, AI enthusiasts, and beginners looking to dive into the world of artificial intelligence. As a cloud-based platform, Google Colab AI Model Training offers users the ability to design, train, and test AI models without the need for powerful local hardware.

This makes it an ideal choice for students and professionals enrolled in AI courses, such as those offered by OHSC. Google Colab is especially well-suited for beginners in AI and data science. With its user-friendly interface and seamless integration with Python libraries like TensorFlow, Keras, and PyTorch, it allows users to get started quickly with AI projects. Whether you are building a simple machine learning model or developing complex deep learning algorithms, Google Colab provides the resources needed to bring your ideas to life. Key Benefits of Google Colab's Free Resources At OHSC, we integrate Google Colab AI Model Training into our comprehensive AI courses, allowing students to learn hands-on through real-world applications and practice.

Whether you’re a beginner or looking to enhance your skills, Google Colab offers a supportive environment to boost your learning journey. Creating a Google Account and Accessing Google Colab

How To Use Google Colab 02 Machine Learning For Beginners

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