Dart for Machine Learning: An Overview

Are you looking for a programming language that can help you build machine learning applications? Look no further than Dart! Dart is a modern, object-oriented programming language that is designed to be fast, efficient, and easy to use. In this article, we will explore how Dart can be used for machine learning and provide an overview of some of the key features of the language.

What is Machine Learning?

Before we dive into the specifics of using Dart for machine learning, let's first define what machine learning is. Machine learning is a type of artificial intelligence that allows computers to learn from data and make predictions or decisions based on that data. Machine learning algorithms can be used for a wide range of applications, from image recognition to natural language processing.

Why Use Dart for Machine Learning?

Dart is a great choice for machine learning for several reasons. First, it is a fast and efficient language that can handle large datasets and complex algorithms. Second, it is easy to learn and use, making it a great choice for developers who are new to machine learning. Finally, Dart has a growing community of developers who are actively working on machine learning projects, which means that there are plenty of resources and support available.

Key Features of Dart for Machine Learning

Now that we've established why Dart is a great choice for machine learning, let's take a closer look at some of the key features of the language that make it well-suited for this type of application.

Strong Typing

Dart is a strongly typed language, which means that variables must be declared with a specific data type. This can help prevent errors and make code more reliable. In machine learning applications, strong typing can be particularly useful for ensuring that data is properly formatted and that algorithms are working as expected.

Asynchronous Programming

Dart has built-in support for asynchronous programming, which allows developers to write code that can handle multiple tasks at once. This can be particularly useful for machine learning applications, which often involve processing large amounts of data in parallel.

Garbage Collection

Dart has a garbage collector that automatically frees up memory that is no longer being used. This can help prevent memory leaks and make code more efficient. In machine learning applications, where large datasets can quickly consume memory, garbage collection can be particularly important.

Libraries and Packages

Dart has a growing number of libraries and packages that can be used for machine learning applications. Some popular packages include the Tensorflow library, which provides support for deep learning, and the MLKit package, which provides support for machine learning algorithms such as decision trees and k-means clustering.

Getting Started with Dart for Machine Learning

If you're interested in using Dart for machine learning, there are several resources available to help you get started. The official Dart website provides a number of tutorials and guides for beginners, as well as more advanced topics such as asynchronous programming and garbage collection.

In addition to the official documentation, there are also a number of third-party resources available. The Dart subreddit is a great place to ask questions and get help from other developers, while the Dart package repository provides a wide range of packages and libraries that can be used for machine learning applications.


Dart is a powerful and versatile programming language that is well-suited for machine learning applications. With its strong typing, support for asynchronous programming, and garbage collection, Dart provides a reliable and efficient platform for building machine learning algorithms. Whether you're a seasoned developer or just getting started with machine learning, Dart is definitely worth considering as a language of choice.

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Written by AI researcher, Haskell Ruska, PhD (haskellr@mit.edu). Scientific Journal of AI 2023, Peer Reviewed