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Choosing the Right Programming Language for Your Machine Learning Project

Choosing the Right Programming Language for Your Machine Learning Project

When picking the best programming language for an ML project, the first thing you need to understand is that there’s no one-size-fits-all manual or a neat step-by-step guide. As cliché as it may sound, the choice largely depends on your project’s specific requirements. At the same time, what you can and should do is research and navigate the current landscape in the field of AI and ML. It will help you choose the right technology and, consequently, expert engineers capable of building effective ML models.

As a recruitment company with years of experience, we at Bridge know the challenges related to sourcing qualified developers and know how to avoid them. For instance, when working with Obvious.ly, we sourced 5-10 data analysts and data scientists a week. If you are looking to find software engineers for your ML project, but don’t know which technology would be the most suitable one, we will gladly share our expertise on how to choose a suitable programming technology. This way, it will be much easier for you to make the final choice. 

So, let’s not waste any more time and dive in.

Top machine learning languages to consider

In plain language, machine learning is a type of programming that enables computers to automatically learn from data provided to them. However, as each project is different, and, therefore, requires a different approach to the development process, we’ve prepared a list of the most popular programming languages that fit different types of projects. Let’s start with Python (where without it):


Because of its readability, simplicity, and ability for fast prototyping, Python heads the list of the most popular programming languages for machine learning. It comes with a variety of libraries built for ML tasks, which provide pre-written code for various functionalities, reducing the need for developers to start from scratch. For instance, scikit-learn is used for data mining and analysis, while TensorFlow provides comprehensive support for high-level neural networks. Such frameworks as Keras, Pandas, Matplotlib, and NLTK are also widely used when programming with Python.

Another benefit of using Python for ML development is its platform independence, which means it seamlessly runs on all platforms, including Windows, Linux, and MacOS. Besides, Python is also one of the most popular programming languages according to Statista, so the cost of development might not be high when compared to rarely-used development languages.

Apart from AI projects, Python is frequently used in IoT, game development, data science, and more, while such world-known companies as Amazon, Uber, Spotify, and others build their products with Python. If you’re planning to join them and hire top Python developers, check out our list of interview questions that can help you find the right expert:

25 Python Interview Questions to Hire the Best Developers for Your Team


R is a powerful programming language and environment specifically designed for statistical computing and graphics. While Python is a popular choice for machine learning, R has its own strengths and unique features that make it a compelling option for certain ML tasks. It offers a variety of libraries to streamline ML development, such as Caret, E1071, RandomForest, Rpart, and others. 

R has an active community of developers who regularly create extra tools (packages) for various needs, including machine learning. It makes the development process fast and cost-effective. What’s more, R is a great solution for projects that involve data wrangling (cleaning, structuring, and organizing raw data) and visualization, predictive modeling, natural language processing, image recognition, and more.


C++ programming language is known for its efficiency and high performance. If your ML project involves computationally intensive tasks, such as large-scale data processing or training complex models, C++ can offer fast execution speeds. It also has an extensive choice of libraries for AI and ML, including MLpack, Shark, Dlib, and others.

Yet, compared to Python or Java, C++ has a steeper learning curve, which makes it a more complex technology to master, so the pool of developers proficient in C++ might not be that wide. On top of that, C++ libraries are not known for their user-friendliness, especially in comparison with the above-mentioned Python.

Still, many world-known tech giants use the C++ programming language for building significant products today. For example, the most popular databases MySQL and Postgres are written with C/C++, while Google used C++ to develop Google Chrome.


Java is not as commonly associated with machine learning as languages like Python or R, but it has its own set of advantages and use cases for ML applications. For starters, it is known for its “write once, run everywhere” philosophy, which means it smoothly runs on various platforms. Moreover, it offers capabilities for handling large amounts of data, which is exactly what is needed for ML apps. Java has a set of great libraries for ML, such as Weka, Mallet, ELKI, Deeplearning4j, and others.

How to Hire Remote Java Developers for Fast-Growing Companies

Many businesses already have existing systems and applications built in Java, so integrating machine learning into these environments may be more seamless with Java. For example, a world-known streaming service Netflix uses Java as its core technology for developing content management systems, recommendation engines, and data processing pipelines.


JavaScript, often linked to web development, has recently become a powerful tool in the field of machine learning. It allows building models and applications that smoothly operate in web browsers, eliminating the necessity for extra software or hardware. Besides, JavaScript is a programming language with abundant documentation and a huge community, so finding resources and support is not a problem when needed. 

Lastly, JavaScript has a lot of tools and libraries for ML development. For instance, TensorFlow.js is used for voice and image recognition, video detection, and text-based apps. Brain.js, in turn, works great for building neural networks, in-browser games, character recognition, and more. 

Machine learning programming languages: comparison table

To help you better navigate the programming languages of machine learning and AI, we’ve summarized everything in the table below:

NameOSCommunity sizeLibraries and frameworks
PythonCross-platform15.7 million developersTensorFlow, scikit-learn  Keras, Pandas, Matplotlib, NLTK
RCross-platform, Windows macOS, and Linux are the best choicesAround 50 thousand developers worldwideCaret, E1071, RandomForest, Rpart, XGBoost, mlr
C++Cross-platform11 million developersMLpack, Shark, Dlib, OpenNN, Shogun, Armadillo
JavaCross-platform14 million developersWeka, Mallet, ELKI, Deeplearning4j
JavaScriptCross-platform17.4 million developersTensorFlow.js, Brain.js, ConvNetJS, Deeplearn.js


No doubt that picking the right programming language is important for your project’s success, so make sure you carefully consider all factors before making the final decision. Assess your project’s specific needs, like performance, scalability, integration, and more. Check if your team already knows the language well as it can make things way smoother. 

When your in-house engineers are not familiar with a language you need, think about whether it’s feasible to upskill them or bring in someone who matches your requirements for the ML project. In case you choose the second option, we at Bridge have essential expertise in sourcing and hiring software engineers across the globe. We can help you in two cases ‒ 1) when you want to add extra talent to your team as fast as possible and 2) when you need a list of vetted candidates for future hires.

IT staffing is a good opportunity to engage top developers from around the world to scale up your team fast and cost-effectively. Here is how it works. You create a job requisition on our client-only platform and we start sourcing candidates. We screen applicants and provide you with the best fit for interviewing. Once you choose the most suitable specialists, we hire and help you onboard them. You pay only the developer’s salary eliminating the cost of recruitment and hiring.

Sourcing-as-a-service seems like a good idea when you want to be in charge of the hiring process, but don’t have time for candidate sourcing. In this case, we will do the heavy lifting for you. Using our one-of-the-kind data engine, Bridge’s recruiters validate candidates and provide you with pre-screened engineers for future hires. One more thing ‒ you pay only a monthly fee, a fraction of what a full-time recruiter would cost.

Partner with Bridge to get top-tier engineers for your ML projects!

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