All

Exploring the Best Programming Languages for Machine Learning in 2025

Artificial intelligence refers to the simulation of human intelligence in machines that are programmed to think and learn. Just like humans, AI systems can analyze data, recognize patterns, solve problems, understand natural language, and make decisions. 

Machine learning is a branch of AI that focuses on creating algorithms that help computers learn from data and make predictions. ML systems are not programmed for every specific task. Instead, they get better as they face more data over time.

AI covers a wide array of technologies, and machine learning is one of those areas that specifically focuses on learning from data. So, while all ML falls under the AI umbrella, not everything in AI is ML. Machine learning offers the tools and methods that help AI systems become smarter and more self-sufficient.

What may seem like a straightforward technical decision, like selecting the best functional programming language for Machine Learning, is actually a make-or-break moment for your entire project. With multiple languages at your disposal, you'll want to weigh the pros and cons of each to build models that truly deliver. Consider this: there are certain vital points to ponder when weighing your options.

Efficiency

How fast and resource-efficient do you need your code to be? Some languages are designed for speed and performance, which is crucial for handling large datasets and complex algorithms. If speed is a priority, you might want to lean toward languages like C++ or Java.

Library Support

Check for the availability of libraries and frameworks. A robust library can save you tons of time and effort by providing pre-built functions and tools. Python, for example, is famous for its extensive ML libraries like TensorFlow, Keras, and Scikit-Learn, making it a popular choice among developers.

Community Support

Having a strong community behind a programming language can be a game changer. A vibrant community means plenty of resources, tutorials, and forums where you can ask questions and find solutions. Languages like Python and R have large, active communities that are incredibly helpful for beginners and seasoned pros alike.

Flexibility

Consider how flexible the language is for your projects. Some languages allow for rapid prototyping, while others might be better suited for production-level code. If you anticipate needing to adapt your project frequently, you’ll want a language that can accommodate those changes easily.

Talent Availability

Think about the talent pool. If you’re planning to hire developers or collaborate with others, it’s essential to choose a language that has a broad base of qualified professionals. Popular languages like Python and Java are often easier to find skilled developers for.

Ease of Learning

If you or your team are new to programming or Machine Learning, the ease of learning is crucial. Some languages have simpler syntax and a gentler learning curve, which can help speed up the onboarding process. For example, developers consider Python one of the most user-friendly languages for newcomers.

Check out our guide: Artificial Intelligence Software Development Guide to learn how to implement AI effectively in your projects.

Each language comes with its unique strengths and is suited to different types of tasks in the Machine Learning landscape. In this section, we'll explore five of the best programming languages for ML in 2025 and highlight what makes each one a strong contender to help you hit the ground running.

Let’s start with the statistics. Take a look at the market share for each of the 5 best programming languages for ML as of 2024 according to Statista:

And now, let’s delve deeper into each language.

Python

Python is a high-level, versatile programming language known for its readability and ease of use. Its syntax is straightforward, making it an excellent choice for both beginners and experienced developers. With a rich ecosystem of libraries and frameworks, Python has become the go-to language for machine learning and data science.

Reasons to choose Python for ML:

• Python’s clear syntax and structure allow developers to focus more on solving problems than on dealing with complex code;
• The abundance of libraries tailored for machine learning makes Python incredibly powerful for data analysis, modeling, and deployment;
• A large community offers plenty of resources and support for developers at all skill levels.

Popular Python libraries:

• TensorFlow – a comprehensive library for building and training Machine Learning models, particularly deep learning applications;
• Keras – an easy-to-use library that sits on top of TensorFlow, ideal for quickly building neural networks;
• Scikit-Learn – a versatile library for classical Machine Learning algorithms and tools for model evaluation and data preprocessing;
• Pandas – a data manipulation and analysis library that provides data structures for efficient handling of large datasets;
• NumPy – a library that supports large, multidimensional arrays and matrices, along with a collection of mathematical functions.

R

R is a language specifically designed for statistical analysis and data visualization. It’s widely used in academia and among statisticians, providing an environment for data manipulation and graphical representation. R’s strengths lie in its extensive collection of packages tailored for data science.

Reasons to choose R for ML:

• R excels in statistical modeling and is ideal for projects that require in-depth analysis and visualization of data;
• The Comprehensive R Archive Network (CRAN) hosts thousands of packages, enabling users to perform almost any data-related task;
• R’s visualization libraries make it easy to create high-quality graphs and charts, enhancing data interpretation.

Popular R libraries:

• caret – a package for training and evaluating Machine Learning models with a consistent interface across different algorithms;
• randomForest – a popular library for implementing the Random Forest algorithm, which is useful for classification and regression tasks;
• ggplot2 – a powerful visualization library that helps create aesthetically pleasing and informative graphics;
• dplyr – a data manipulation library that makes it easier to transform and summarize datasets;
• glmnet – a package that effectively handles high-dimensional datasets and provides cross-validation for optimizing model parameters.

Java

Java is a robust, object-oriented programming language that is platform-independent, meaning it can run on any device with a Java Virtual Machine (JVM). Its reliability and scalability make it a popular choice for large-scale enterprise applications, including Machine Learning.

Reasons to choose Java for ML:

• Java’s “write once, run anywhere” capability allows developers to deploy Machine Learning models across various platforms easily;
• Java is known for its speed and efficiency, making it suitable for processing large volumes of data;
• With a wealth of libraries and frameworks, Java can support a wide range of Machine Learning tasks.

Popular Java libraries:

• Weka  – a collection of Machine Learning algorithms for data mining tasks, with an easy-to-use interface;
• Deeplearning4j – a deep learning library designed for Java, compatible with Hadoop and Apache Spark;
• ADAMS – a distributed computing framework that supports large-scale data processing and Machine Learning;
• Apache OpenNLP – a toolkit for natural language processing tasks like tokenization, tagging, and entity recognition;
• MOA (Massive Online Analysis) – a framework for online learning and data stream mining.

Scala

Scala combines functional and object-oriented programming principles. It runs on the Java Virtual Machine (JVM) and is known for its concise syntax and compatibility with Java, making it a great option for big data and ML applications.

Reasons to choose Scala for ML:

• Scala is a strong choice for handling large datasets in machine learning;
• The language’s concise syntax allows for more expressive code, enabling developers to write less while achieving more;
• Scala’s strong static type system helps catch errors at compile-time, which can lead to more robust code;

Popular Scala libraries:

• Spark MLlib – a scalable machine learning library built on top of Apache Spark, providing tools for classification, regression, clustering, and more;
• Scalaz – a library that enhances functional programming capabilities in Scala, facilitating more complex machine learning models;
• Akka – a toolkit for building concurrent applications, useful in distributed ML environments;
• Saddle – a data manipulation library that provides high-performance data structures and tools for statistical computing;
• Smile – an ML library that offers algorithms for classification, regression, clustering, and more, with a focus on performance and ease of use

C++

C++ is one of the fastest languages for machine learning that offers fine-grained control over system resources. It is known for its efficiency and low-level control, so developers often use it in scenarios where speed and performance are critical. This makes C++ one of the most suitable languages for deep learning and machine learning applications that require intensive computation.

Reasons to choose C++ for ML:

• C++ is known for its speed, making it ideal for computationally intensive Machine Learning algorithms;
• The language provides fine-grained memory management, allowing developers to optimize the usage of resources and manage memory-intensive operations;
• C++ is highly portable, allowing ML applications to run across different operating systems and devices, including embedded systems and IoT devices where resources are limited.

Popular C++ libraries:

• Dlib – a modern C++ toolkit that contains machine learning algorithms and tools for image processing and computer vision;
• Shark – a fast and modular library for machine learning, providing various optimization methods and algorithms;
• OpenCV – an open-source library that offers machine learning algorithms that can be leveraged for image and video analysis;
• MLpack – a scalable C++ machine learning library that emphasizes speed and ease of use;
• TensorFlow C++ API – the C++ interface for TensorFlow, allowing users to leverage TensorFlow's capabilities directly in C++ projects.

When it comes to choosing a programming language for machine learning, it helps to look at each language’s unique strengths and how they align with your project’s goals. Some languages are better for prototyping and data handling, while others excel in performance and scalability. 

Here’s a quick comparison to help you pick the best one for your needs.

SapientPro has 7+ years of experience in machine learning development, working with businesses to harness the power of their data and make it actionable. No matter the request, we’ve taken on a range of projects tailored to meet real-world challenges. Our approach is hands-on and collaborative, ensuring that each solution fits your  needs, regardless of how complex the project is.

SapientPro focuses on practical, scalable solutions that teams can easily manage and adjust over time. We have a deep understanding of what businesses require from their ML models, from streamlined data processing to real-time insights. This commitment to bridging technical complexity with user-friendly design is what helps us bring value to your business.

Take a look at some of our clients’ success stories where AI played a crucial role – creating an AI-powered data analytics platform and developing a decentralized domain naming system.

Choosing the right programming language for Machine Learning comes down to understanding the strengths and limitations of each one in relation to your project’s goals. Python’s versatility, R’s data prowess, Java’s scalability, Scala’s big data support, and C++’s speed all offer unique advantages for different ML needs. The language and the AI development services provider you choose can make a real difference in your project’s efficiency and success, so take the time to find the best match.

What is the best programming language for machine learning?

The best programming language for ML is the one that fulfills your specific project needs. However, one of the best programming languages for machine learning are Python, R, Java, Scala, C++.

Is Python better than C++ for machine learning?

Python is generally preferred for its simplicity and extensive libraries, making it easier to develop machine learning models quickly. C++, while faster in execution, has a steeper learning curve and is typically used for performance-critical applications. The choice depends on the specific project requirements and the developer's familiarity with the languages.

Is R replacing Python in machine learning?

Not exactly. While R is popular for statistical analysis and data visualization, Python remains the dominant choice for machine learning, thanks to its versatility and broader application.

What is the fastest programming language for machine learning?

C++ is typically considered one of the fastest programming languages for machine learning due to its low-level capabilities and high performance, especially in computationally intensive tasks.

Where to hire machine learning developers?

The best option is to hire from a reliable agency that specializes in machine learning like SapientPro. These agencies typically have a pool of experienced developers with proven expertise. Contact us to discuss your project.