Top 6 Programming Languages for Machine Learning in 2025

Machine learning (ML) is changing the way we solve problems, from making better predictions to creating smart technologies like self-driving cars.

The programming language you choose plays a big role in how smoothly and effectively you can build machine learning projects.

In this article, we’ll look at the best ML programming languages, what they’re good at, how they’re used, and what makes them popular.

Table of Contents Table of Contents

1. Key Takeaways
2. What Are Machine Learning Programming Languages?
3. 6 Best Programming Languages for Machine Learning
4. The Bottom Line
5. FAQs
6. References

Show Full Guide

Table of Contents

1. Key Takeaways
2. What Are Machine Learning Programming Languages?
3. 6 Best Programming Languages for Machine Learning
Show Full Guide
4. The Bottom Line
5. FAQs
6. References

What Are Machine Learning Programming Languages?

Machine learning programming languages are used to create systems that can learn from data and make decisions or predictions without needing specific instructions for every task. They help developers build algorithms and models to recognize patterns, analyze information, and improve over time as they process more data.

For example, these languages enable various applications, including speech recognition, recommendation systems, fraud detection, and self-driving cars.

Popular languages, such as Python, R, and Java, are used because they have libraries and tools that make it easier to work with data and train machine learning models efficiently.

6 Best Programming Languages for Machine Learning

Let’s take a look at the six best ML programming languages.

1. Python

Python is a popular programming language used in machine learning because it’s simple to learn and has powerful tools.

It offers libraries, such as TensorFlow, PyTorch, and Scikit-learn, to help developers easily create and train machine learning models for various tasks, such as predicting trends, recognizing images, or analyzing text. Python has a large and active machine learning community, making it a popular choice for ML development.

Raj Sonani, senior AI product manager at LexisNexis, told Techopedia:

“Python has become one of the most popular programming languages in the world. Its widespread use in fields like data science, artificial intelligence, machine learning, and the Internet of Things highlights its incredible versatility. Its popularity is evident, with over 80% of AI professionals choosing Python for ML projects.”

However, as an interpreted language, Python can be slower than C++ or Java, which may impact real-time or high-performance applications, he added.

2. R

R is a programming language used for analyzing data and performing statistics, which makes it a good fit for machine learning. It has many tools and libraries, such as Caret, to help create and test machine learning models.

With R, developers can prepare data, build models to make predictions, group similar items, and easily create visual charts to understand the results.

R is designed for statistical computing and has many libraries in place for ML and visualization, making it an ideal choice for data science, ML, and data visualization, said Ria Gupta, a software engineer intern at NASA.

However, it has some drawbacks. As Gupta told Techopedia:

“R can have performance issues when working with large datasets since it is interpreted, and also has a steeper learning curve than Python, for example. It also has a smaller community and less support for object-oriented programming.”

3. Java

Java is often used in machine learning because it’s fast, handles big projects well, and has tools, such as Weka and Deeplearning4j, to help create and train models.

Java is used to build applications that analyze data, make predictions, or automate decisions. It also works with big data tools, such as Apache Hadoop and Spark, making it a good choice for working with large amounts of data in machine learning.

Lars Nyman, chief marketing officer at Cudo Compute, told Techopedia:

“Java is very scalable and robust and great for enterprise-level systems. However, it’s pretty verbose and lacks deep learning-focused libraries.”

Jacob Anderson, owner of Beyond Ordinary, a software consultancy and research company, said while there is quite a bit of AI/ML support, it’s not as easy to innovate.

“The [Java] language, which tends to be verbose and clunky sometimes, shows its age,” he said.

4. Julia

Julia is a programming language built for speed and ease of use, making it great for machine learning. It’s fast like C but as simple to write as Python, which helps when working with large datasets and complex math.

Julia is popular for machine learning because it has tools to work with numbers, can do multiple tasks at once to save time, and works well with libraries for analyzing data and building deep learning models.

Julia has tools, such as Flux.jl and MLJ.jl, that make it easy to create models for predicting outcomes or recognizing patterns.

“Julia is fast and ideal for high-performance numerical tasks,” said Nyman. “But, it’s still a developing ecosystem and community.”

Sonani said that Julia bridges the gap between high-level and low-level programming.

“It delivers performance comparable to C++ while maintaining a syntax that is clean and easy to read, making it ideal for intensive AI tasks.”

5. Scala

Scala is another programming language well-suited for machine learning. It combines different programming styles and is often used with Apache Spark, a tool for handling large amounts of data, which is built using Scala.

Scala works well with Java, making it easy to use existing tools and libraries. It’s popular for machine learning because it helps process big data quickly and makes building and scaling models easier.

“Scala is great for big data and distributed machine learning (with Spark),” said Nyman. “Arguably, it’s hard to learn, and there’s limited support for rapid prototyping.”

Gupta says that Scala can be integrated with Apache Spark, which is used for large-scale distributed ML tasks and has functional and object-oriented programming capabilities.

“[However], picking up Scala can be a steeper learning curve than Python, and it has a smaller set of libraries as well,” she said.

6. C++

C++ is a fast and efficient programming language often used in machine learning for tasks that need high performance, such as training deep learning models or running real-time applications.

Many machine learning libraries, such as TensorFlow and PyTorch, use C++ because it makes them faster. Developers use C++ to build custom models, improve algorithms, and add machine learning features to larger systems.

“C++ offers high performance and memory efficiency, making it ideal for resource-intensive machine learning tasks,” said Sonani. “However, its complex syntax and manual memory management can be challenging for beginners. While it excels in real-time systems and large-scale AI applications, it may not be the best choice for rapid prototyping, where languages like Python are more commonly used.”

Gupta agrees.

“C++ is very fast, which makes it efficient for computationally intensive tasks,” Gupta said. “It also offers low-level control over memory and system resources, like the GPU, which helps with image recognition and other ML tasks. Also, major libraries like Tensorflow are implemented in C++.”

However, C++ has a very steep learning curve and development can be slower since debugging and programming often involve manual memory management, whereas Python and other languages handle this for the developers, she added.

The Bottom Line

Choosing the right programming language is a key step in building successful machine learning projects. Each of these top programming languages for ML has its strengths and is suited for different tasks, whether it’s handling massive amounts of data, creating quick prototypes, or building complex models.

By understanding what each language offers, you can pick the one that best fits your needs and goals.

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