A Beginner’s Guide to Visual Studio: What is it and How to Get Started with AI Projects

What is Visual Studio? A Beginner-Friendly Introduction

Visual Studio is a powerful integrated development environment (IDE) created by Microsoft. It is designed to help developers write, edit, test, and debug code efficiently across a wide range of programming languages like C++, Python, JavaScript, and C#. It supports multiple development types, including web, desktop, mobile, gaming, and cloud-based applications making it one of the most versatile IDEs available today.

Whether you're a beginner or a seasoned professional, Visual Studio provides a user-friendly interface with advanced features tailored for productivity. If you’re wondering what is a visual studio, think of it as a one-stop solution for software development that simplifies the entire coding process while offering powerful tools under the hood.

Key Features for Developers and AI Professionals

Visual Studio is packed with intelligent features that boost productivity. Some standout capabilities include:

• Code completion and IntelliSense: Predicts and auto-completes code as you type.
• Integrated debugger: Lets you find and fix errors with ease.
• Git integration: Seamless version control directly within the IDE.
• Live Share: Enables real-time collaboration with other developers.
• Extensions and AI tools: Supports AI-focused extensions like Python tools for AI model building or integration with Azure AI services.

Whether you're building machine learning models or creating full-stack web applications, Visual Studio can adapt to your workflow.

Ideal Use Cases for Visual Studio

Visual Studio is best suited for:

• Professional software development with large-scale enterprise projects.
• AI and data science projects, especially when using languages like Python or integrating with cloud services.
• Cross-platform development, including Android, iOS, and web apps using Xamarin or .NET MAUI.

For developers who want a complete, professional-grade environment, Visual Studio offers robust tools and flexibility for any project. It’s trusted by beginners and advanced users alike for its smart features, clean interface, and strong community support.
Getting started with Visual Studio is easy—and it can scale with you as your coding skills grow.

What is Visual Studio Code? Understanding this Lightweight Power Tool

Visual Studio Code, commonly it is known as VS Code, is a free, open-source code editor developed by Microsoft. It’s designed to be lightweight yet powerful, offering a streamlined coding experience without the heavy system load of full IDEs. It supports a wide range of programming languages, including Python, JavaScript, C++, and more. If you’re wondering what is visual studio code, it’s essentially a fast, flexible, and highly customizable code editor built for modern developers.

VS Code is ideal for both beginners and experienced programmers. Its small installation size and quick performance make it perfect for those who want to code efficiently without dealing with unnecessary complexity. Despite being lightweight, it offers many features typically found in full-fledged IDEs.

Why it’s Popular Among AI Learners and Python Developers?

VS Code has become a favorite among AI learners and Python developers for several reasons. First, it supports Python out of the box and offers excellent integration with tools like Jupyter Notebooks, TensorFlow, PyTorch, and scikit-learn. Second, the Python extension in VS Code includes IntelliSense (smart code suggestions), debugging tools, and environment management—all essential for AI and data science projects.

Moreover, the editor’s versatility allows AI enthusiasts to seamlessly switch between coding, experimenting with models, and collaborating through Git integration and Live Share. Its compatibility with virtual environments and conda environments further enhances the AI development experience.

Core Features that Support AI Workflows

VS Code includes many productivity-boosting features tailored for AI workflows:

• Built-in terminal for running scripts and commands
• Notebook support for working with Jupyter directly inside the editor
• Extension marketplace for installing AI-related tools like Python, Pylance, Docker, and more
• Customizable interface with themes and keyboard shortcuts

Thanks to its speed, flexibility, and strong ecosystem, VS Code is the go-to tool for developers exploring AI, automation, and beyond.

What is Visual Code? Clearing Up the Confusion

The term Visual Code is often used informally or mistakenly when people refer to Microsoft’s popular code editor, Visual Studio Code. Because the names are quite similar, many beginners and even some experienced developers may shorten it to “Visual Code” in casual conversation or online discussions. However, Visual Code itself isn’t an official product name—it's usually just a shorthand or typo for Visual Studio Code.

Difference Between Visual Code and Visual Studio Code

To clarify, Visual Code (often called VS Code) is a lightweight, free, and open-source code editor designed for quick editing and debugging. It supports many programming languages and has a rich extension ecosystem. On the other hand, Visual Code is not a standalone tool or software; it’s simply a mistaken or abbreviated term people use when referring to VS Code.

This confusion also arises because of other Microsoft products like Visual Studio, which is a full-featured integrated development environment (IDE) used for large-scale software development. 

Importance of Using the Correct Terminology

Using the right terminology is important to avoid misunderstandings, especially when discussing development tools or seeking help. Referring correctly to Visual Studio Code ensures you get accurate resources, support, and downloads without confusion. Knowing the difference between Visual Studio, Visual Code, and the informal Visual Code helps streamline communication among developers and learners.

So next time you hear “Visual Code,” remember it’s likely shorthand for Visual Studio Code, and using the proper name will keep things clear and precise. Understanding what is visual code helps avoid mix-ups and improves learning and collaboration.

Visual Studio Vs Visual Studio Code: Which One Should you Choose for AI Projects?

When starting AI projects, many developers wonder whether to use Visual Studio or Visual Studio Code. While they come from the same company—Microsoft—they serve different purposes. What is a Visual Studio is a powerful, full-featured integrated development environment (IDE), ideal for large-scale enterprise applications, whereas Visual Studio Code is a lightweight, fast, and flexible code editor preferred by those who want speed and simplicity.

Visual Studio for Large-Scale AI Projects

Visual Studio shines in enterprise environments where you need advanced features like deep debugging, Azure integration, and team collaboration tools. It supports .NET-based AI tools like ML.NET and offers robust tools for working with cloud-based AI services. If you're building complex applications with multiple layers—such as APIs, data models, and user interfaces—Visual Studio is the better fit.

Visual Studio Code for Quick, Efficient AI Development

On the other hand, Visual Studio Code (VS Code) is perfect for beginners, students, and independent developers. It supports Python, R, and Jupyter Notebooks through extensions and allows you to experiment with AI models quickly. Its minimal setup, fast performance, and customizable interface make it the go-to choice for writing and testing AI scripts, running models, and doing exploratory data analysis.

Choosing Based on Experience and Project Needs

If you're just starting out in AI or working on lightweight or academic projects, VS Code will likely meet all your needs. If you’re part of a larger team or managing enterprise-level infrastructure, Visual Studio may be more appropriate.

Finally, it’s essential to use the correct terminology to avoid confusion—especially when someone casually asks what is visual code, which is often a mistaken reference to Visual Studio Code. Choosing the right tool ensures you're set up for both efficiency and growth in your AI journey.

How Do you Install Visual Studio for AI Development?

To begin, head over to the official Microsoft website and download the Visual Studio installer. You’ll be presented with multiple versions—Community, Professional, and Enterprise. For most AI learners and developers, the Community Edition is free and more than sufficient. Once the installer is downloaded, run it to begin setup.

Choose the Right Workloads

After launching the installer, you’ll see a screen that allows you to select “Workloads”—predefined groups of tools for specific development tasks. For AI development, choose the following:

• Python Development – Essential for most AI and machine learning projects.
• Data Science and Analytical Applications – Includes tools like Jupyter Notebook support and data visualization packages.
• .NET Desktop Development – Optional, but useful if your AI projects will integrate with desktop apps.

You can always add more workloads later, so don’t worry about getting everything perfect on the first try.

Install and Launch

Click the Install button and wait for Visual Studio to set up your selected features. The installation time will vary depending on your choices and internet speed. Once installed, launch Visual Studio and sign in with your Microsoft account for full access to extensions and cloud tools.

Set Up your AI Environment

After setup, open a new Python project or Jupyter Notebook to verify everything works. Install essential packages like NumPy, Pandas, TensorFlow, or PyTorch via the integrated terminal or environment manager. You now have a robust environment ready for AI experiments and development.

This step-by-step setup ensures your Visual Studio is fully optimized for data science and AI workflows—whether you're a beginner or scaling up to enterprise-level projects.

How Do you Set Up Visual Studio Code for AI Projects?

To get started, go to the official Visual Studio Code website and download the version that matches your operating system (Windows, macOS, or Linux). Once downloaded, install it using the default setup options. After launching, you’ll see a clean and minimal interface, perfect for focused development.

Install Essential Extensions

Visual Studio Code becomes powerful for AI development when you install the right extensions. Open the Extensions panel (or press Ctrl+Shift+X) and search for these key tools:

• Python by Microsoft – Enables Python language support and linting.
• Jupyter – Lets you run interactive Jupyter Notebooks inside VS Code.
• Pylance – Offers intelligent code suggestions and type checking.
• Code Runner (optional) – Run small Python snippets quickly.

These extensions transform VS Code into a robust AI and data science editor.

Set Up Python and AI Libraries

Before writing any code, install Python from python.org if you haven't already. Then, open the terminal in VS Code and run the following commands to install common AI libraries:

pip install numpy pandas matplotlib scikit-learn jupyter tensorflow

These packages form the foundation for most AI projects and workflows.

Create a Simple AI Project Structure

Organize your workspace with a basic folder structure. Create a new folder like this:

ai-project/

│

├── data/

├── notebooks/

├── models/

└── main.py

This structure keeps your code, data, and models cleanly separated, making it easier to scale and collaborate.

With these steps complete, your Visual Studio Code environment is ready to handle everything from basic AI tasks to complex deep learning workflows.

How Do you Start your First AI Project Using Visual Studio Code?

Once you’ve installed the necessary extensions in Visual Studio Code, you can begin by creating either a Python script or a Jupyter Notebook. For a Python script, create a file named main.py. For interactive experiments, especially in AI, a Jupyter Notebook (.ipynb) is ideal.

To create one, click the “New File” icon and select “Jupyter Notebook.” You can now write and execute code in cells—perfect for data exploration, training models, and visualizing results.

Build a Beginner-Friendly AI Project

For your first AI project, start simple. A great beginner example is sentiment analysis using a small dataset of movie reviews or tweets. Another option is image classification using the CIFAR-10 or MNIST dataset.

Here’s a brief sentiment analysis example using Python:

from textblob import TextBlob

text = "I love learning AI with VS Code!"

analysis = TextBlob(text)

print("Sentiment:", analysis.sentiment.polarity)

You can run this code directly in the terminal or notebook. It gives instant feedback and shows how text data can be turned into insights with just a few lines of code.

Run and Test your Code

To run your script, right-click on the editor and select "Run Python File in Terminal." For notebooks, use the “Run Cell” icon. Make sure your environment is activated and all dependencies are installed.

By now, you’re not just learning what is Visual Studio Code, you’re using it to power real-world AI projects—efficiently, interactively, and at your own pace. Starting small builds confidence, and as you progress, you can explore more advanced models and techniques.

What are the Must-Have Extensions and Tools for AI Projects in Visual Studio Code?

Visual Studio Code is powerful on its own, but it becomes an AI development powerhouse with the right extensions. Here are a few must-haves:

• Python – Official extension by Microsoft for Python language support.
• Jupyter – Lets you create and run Jupyter Notebooks directly in VS Code.
• Pylance – Offers rich type checking, smart suggestions, and fast IntelliSense.
• TensorFlow Snippets – Adds quick snippets for TensorFlow, making model building faster.
• Code Runner – Helps you quickly run small chunks of code without needing a terminal.
• Docker – Useful for containerizing your AI projects for consistent environments.

These extensions make it easier to build, test, and iterate on machine learning models right inside the editor.

Using Virtual Environments and Git Integration

Working in a virtual environment keeps your AI project dependencies organized. VS Code detects virtual environments automatically. Use the integrated terminal to set one up:

python -m venv venv

source venv/bin/activate  # On Windows, use venv\Scripts\activate

Git integration is also seamless in Visual Studio Code. Use the Source Control tab to track changes, commit code, and push to GitHub—all without leaving the editor.

Debugging and Productivity Tips

To debug Python scripts, use breakpoints and the built-in debugger (accessible from the Run tab). For notebooks, use “Run by Line” for step-by-step execution.

Customize keyboard shortcuts, split views, and extensions like Todo Tree to manage tasks and improve focus. With these tools, you can transform VS Code into a high-efficiency AI workspace.

From code management to deep learning, Visual Studio Code supports it all—making it a smart choice for any aspiring AI developer.

Conclusion

 Choosing the right tool is key to success in AI development. If you’ve ever wondered what is a visual studio, it’s a comprehensive IDE suited for large-scale applications. In contrast, what is visual studio code refers to a lightweight, flexible editor ideal for beginners and quick AI experiments. Some users also ask what is visual code, often mistaking it for VS Code. Clarity matters when picking tools. To move forward, explore LAI’s free AI courses, try a small project like sentiment analysis, and get familiar with your environment. The right tool—paired with consistent learning—can jumpstart your journey into AI.