Setting Up Your ML Environment: Jupyter, Anaconda, or Google Colab
Before you dive into writing Machine Learning code, you need the right development environment. This environment should be simple to use, flexible for experiments, and equipped with all the necessary libraries and tools.
In this blog, we’ll guide you through setting up your ML environment using the three most popular options:
- Jupyter Notebook
- Anaconda Distribution
- Google Colab
Each of these tools has its own strengths — let’s explore and help you decide which one suits you best.
Why Do You Need a Dedicated ML Environment?
Machine Learning involves working with large datasets, training models, and visualizing results. A good environment allows you to:
- Write and run Python code interactively
- Install and manage ML libraries
- Plot charts and graphs inline
- Share and collaborate on code easily
1. Jupyter Notebook
Jupyter Notebook is an open-source web application that allows you to write code and see outputs inline. It supports Python and other programming languages.
Key Features
- Interactive coding in cells
- Supports code, visualizations, and markdown notes
- Great for experiments and tutorials
- Widely used in data science and ML
How to Install
Jupyter is automatically installed if you use Anaconda (see below). If you prefer to install it manually:
pip install notebook
Then start it with:
jupyter notebook
Your browser will open a local server with a dashboard to create .ipynb files.
2. Anaconda Distribution
Anaconda is a popular Python distribution that comes pre-loaded with:
- Jupyter Notebook
- Spyder IDE
- NumPy, Pandas, Scikit-learn, Matplotlib, and more
It’s the easiest way to set up a complete ML environment offline.
Key Features
- One-click install for hundreds of ML/data science libraries
- Comes with Anaconda Navigator (GUI to manage tools)
- Manages packages and environments using
conda - Ideal for offline and multi-library projects
How to Install
- Go to https://www.anaconda.com/download
- Download the installer for your OS
- Install and open the Anaconda Navigator
- Launch Jupyter Notebook or Spyder from the dashboard
Creating a new environment
conda create --name ml-env python=3.11
conda activate ml-env
3. Google Colab
Google Colab is a cloud-based Jupyter Notebook environment provided by Google. It’s free, runs on the cloud, and requires no installation.
Key Features
- No setup needed — runs in your browser
- Free access to GPU and TPU
- Easy to share notebooks like Google Docs
- Great for quick experiments and teaching
How to Start
- Go to https://colab.research.google.com
- Click “New Notebook” to start coding
You can also import notebooks from GitHub or Google Drive.
Comparison Table
| Feature | Jupyter Notebook | Anaconda | Google Colab |
|---|---|---|---|
| Installation | Yes | Yes (includes Jupyter) | No (browser-based) |
| Runs Locally? | Yes | Yes | No |
| GPU Support | No (default) | Limited | Yes (free GPU/TPU) |
| Internet Required | No | No | Yes |
| Best For | Beginners, Explorers | Offline ML projects | Students, Online learners |
| Package Management | pip | conda | pip |
Which One Should You Use?
- Use Google Colab if you want to start instantly and don’t want to install anything.
- Use Anaconda if you want an all-in-one local setup with powerful package management.
- Use Jupyter Notebook if you prefer lightweight, local notebooks with full control.
Most ML professionals use a combination of Anaconda + Jupyter Notebook locally, and Google Colab for sharing and running heavier models on cloud GPUs.
Conclusion
Choosing the right ML environment is your first step toward becoming a successful machine learning developer. Whether you prefer working locally with Anaconda or on the cloud with Google Colab, each tool helps you learn and build faster.