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Getting Started: Setting Up Your Environment

Getting Started: Setting Up Your Environment

Welcome to the Python Essentials for AI Applications tutorial! To follow along with the code examples and complete the exercises, you’ll need a Python environment with specific libraries installed.

We recommend using Miniconda to manage your Python environments and packages. Jupyter Notebooks will be our primary tool for interactive coding.

Choose the setup method that works best for you:

Option 1: Local Setup with Miniconda (Recommended)

This gives you a dedicated environment on your own computer.

  1. Install Miniconda:

    • Download the Miniconda installer for your operating system (Windows, macOS, Linux) from the official Conda documentation: https://docs.conda.io/projects/miniconda/en/latest/
    • Follow the installation instructions provided on the download page. Accept the default settings unless you have specific reasons to change them.

  2. Create a Conda Environment:

    • Open your terminal (Anaconda Prompt on Windows, Terminal on macOS/Linux).
    • Create a new environment specifically for this tutorial. We’ll name it pyai_env and install Python 3.10 (or a recent version):
      conda create --name pyai_env python=3.10 -y
    • Activate the new environment:
      conda activate pyai_env
      You should see (pyai_env) appear at the beginning of your terminal prompt.

  3. Install Jupyter Notebook:

    • With the pyai_env environment active, install Jupyter:
      conda install jupyter -y

  4. Install Core Libraries (Optional - can be done later):

    • You can pre-install the main libraries we’ll use now, or install them as needed within the tutorial notebooks:
      conda install numpy pandas scikit-learn matplotlib seaborn pytorch torchvision torchaudio cpuonly -c pytorch -y
conda install opencv-python -y 
# For NLP libraries, we might install others later (e.g., nltk, transformers)

    Note: The PyTorch command installs the CPU-only version. If you have a compatible NVIDIA GPU and want GPU acceleration, refer to the official PyTorch installation instructions for the correct CUDA-enabled command.

  5. Launch Jupyter Notebook:

    • Make sure your pyai_env is active in the terminal.
    • Navigate to the directory where you want to save your tutorial notebooks (or where you cloned the tutorial repository if applicable).
    • Run the command:
      jupyter notebook
    • This will open the Jupyter Notebook interface in your web browser. You can now create new notebooks or open existing .ipynb files.

Option 2: Google Colaboratory (Colab)

If you prefer not to install software locally or want a cloud-based solution, Google Colab provides a free Jupyter Notebook environment with many common AI libraries pre-installed.

  • Access Colab: Go to https://colab.research.google.com/
  • Usage: You can create new notebooks or upload notebooks from your computer or GitHub.
  • Installation: While many libraries are pre-installed, you might occasionally need to install specific packages within a Colab notebook cell using !pip install package-name.
  • Limitations: Be aware of usage limits (GPU/TPU time, RAM) on the free tier. Your environment is also not persistent; you’ll need to re-run setup cells or reinstall packages if your session disconnects.

With your environment set up, you’re ready to dive into the first module! Proceed to the next section in the documentation.

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