Setup and guide for tools and languages required for AI (artificial intelligence)

Essential Tools and Languages for AI Development

Artificial Intelligence (AI) development requires a combination of tools, programming languages, and development environments to build, test, and deploy models effectively. This guide outlines the necessary and optional resources along with a basic setup process to get started.

Required Tools and Languages

*Tool/Language*	*Required/Optional*	*Version*	*Description*	Link
WSL (Windows Subsystem for Linux)	Optional	Latest	Enables Linux/Ubuntu distros on Windows OS, avoiding Windows-related package management issues.	LINK
Python	Required	Latest	Core development language for AI applications.	LINK
Pip	Required	Latest	Package management tool included by default with Python installations.	LINK
Conda	Optional	Latest	An alternative package management tool.	LINK
Jupyter Notebook	Required	Latest	Development environment for writing and testing AI code interactively.	LINK
Jupyter Lab	Optional	Latest	Enhanced version of Jupyter Notebook with additional features.	LINK
Visual Studio Code	Required	Latest	Versatile code editor and development environment, highly customizable with extensions.	LINK
PyLint	Required	Latest	A plugin for linting Python code to ensure quality and avoid bugs.	LINK

Basic Setup & Installation Guide

Step 1: Enable WSL and Install Ubuntu Distro (Optional)

For Windows users, enabling WSL allows you to run a Linux environment. Install Ubuntu from the Microsoft Store to simplify package management and avoid potential compatibility issues.

Step 2: Install Python 3.11

Download and install Python 3.11 from python.org. Verify the installation with the command:

python -V

Step 3: Verify Pip Installation

Ensure pip, Python’s package manager, is installed by running:

pip -V

Step 4: Set Up a Virtual Environment

Creating a virtual environment is a best practice to avoid package conflicts across projects. Run the following commands to create and activate a virtual environment:

python -m venv myenv

source myenv/bin/activate  # For Linux/Mac

myenv\Scripts\activate     # For Windows

Step 5: Install Jupyter Notebook/Lab or Visual Studio Code

Install Jupyter Notebook or Jupyter Lab using pip:

pip install notebook

pip install jupyterlab

Alternatively, install Visual Studio Code for an integrated development experience.

Step 6: Launch Jupyter Notebook/Lab

Use the following commands to launch Jupyter and access it via your browser:

jupyter notebook

# or

jupyter lab

If using WSL, run the commands in the Ubuntu terminal and open the provided localhost URL in your browser.

Step 7: Explore AI Frameworks

Start experimenting with AI frameworks like:

• OpenAI: For GPT models and other generative AI tools.

• LangChain: For building language model applications.

• LlamaIndex: For indexing and querying data.

By setting up these tools and environments, you’ll have a solid foundation to begin your AI development journey. Start exploring frameworks and build AI-powered solutions to bring your ideas to life.