Setting Up Google Colab and Google Drive

Upload and connect your class files to Google Colab and Google Drive to start using Jupyter Notebooks.

Set up your machine learning journey efficiently by integrating Google Colab with your Jupyter Notebooks. Learn the straightforward steps to seamlessly connect Google Drive, enabling convenient cloud-based access to your Python machine learning files.

Key Insights

  • Download the provided class files and upload the initial Jupyter Notebook—ml10 stats start—to Google Colab, ensuring all materials are stored and accessed in the cloud.
  • Establish a connection between Google Colab and Google Drive by executing the first code cell, authorizing file access permissions for seamless workflow going forward.
  • Using Google Colab and Google Drive consistently will enable you to conveniently access, manage, and run Jupyter Notebooks from any location or device.

Note: These materials offer prospective students a preview of how our classes are structured. Students enrolled in this course will receive access to the full set of materials, including video lectures, project-based assignments, and instructor feedback.

Hello everybody, we're going to walk through our setup for how to get started with our class files. The very first thing you're going to do is download our class files. It should come in a ZIP archive that you can extract. Once you've got a folder, what we're going to do is use Google Colab to open the very first Jupyter Notebook, and we'll talk about what a Jupyter Notebook is momentarily. But you are going to want to be using Google Colab and Google Drive. All our examples will use that setup, and it's a good setup—that's why we're using it. So you'll get to know Google Colab and how it works, and you'll be able to use your Jupyter Notebooks on any machine because they'll live in the cloud.

Okay, let's walk through what that process looks like just to get started. It should be easier from then on, but just to get your files into your Google Colab and Google Drive accounts. The very first thing you should do is go to Google Colab. Once you're there, it first prompts you to open a notebook. We're going to choose Upload and Browse, and then you're going to navigate your way to your folder, which should be called Python Machine Learning Bootcamp, to the Start folder within there, and let's open up ML10_Stats_Start. That's the first notebook we'll be working with. When you choose Open, Google Colab will upload that file, and now it’s part of your Google Colab collection.

So once you're there, one of the very first things—before we actually cover some material—is to get things started. Our very first cell block—our very first code cell—is the one that sets up a connection between your Google Colab and your Google Drive. I'd like you to run that cell, and you do that by pressing the play button. It's going to take a moment, and then it's going to prompt you to connect Google Drive to this particular notebook. But let's do that. Let's see what that looks like. It's getting Python started, which always takes about 10–15 seconds the first time you run a new notebook. Didn’t even take that long. Then you're going to get this message to permit this notebook to access your Google Drive files. You'll get this message for each one if you haven’t already set up a Google Drive–Colab connection. This process will be slightly more complicated.

It'll just have a little checkbox. You'll choose "Select All, " and you'll give Google all the permissions to access your Google Drive files from Google Colab. Once you've done it the first time, it looks just like this—with only a couple of "Continue" buttons. Then this should get a little check mark. If this gets a check mark, it means that your Google Colab and your Google Drive are connected. Now that they're connected, we can upload all of our files at once to Google Drive and be able to access them and switch over to Google Colab for any notebook files.

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We'll do that in the next video.

Colin Jaffe

Colin Jaffe is a programmer, writer, and teacher with a passion for creative code, customizable computing environments, and simple puns. He loves teaching code, from the fundamentals of algorithmic thinking to the business logic and user flow of application building—he particularly enjoys teaching JavaScript, Python, API design, and front-end frameworks.

Colin has taught code to a diverse group of students since learning to code himself, including young men of color at All-Star Code, elementary school kids at The Coding Space, and marginalized groups at Pursuit. He also works as an instructor for Noble Desktop, where he teaches classes in the Full-Stack Web Development Certificate and the Data Science & AI Certificate.

Colin lives in Brooklyn with his wife, two kids, and many intricate board games.

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