In 9 out of 10 articles on our blog, you’ll find mentions of artificial intelligence and machine learning (ML). That’s because these are no longer just technologies of the future—they’re a new way of doing business.
While this article is broadly about “best programming languages for machine learning,” it’s NOT about how to write code. We want to show you how to improve processes and increase profits. Even a basic understanding of ML capabilities can help you:
- Identify areas where ML can be beneficial;
- Communicate effectively with experts and contractors;
- Invest in the right technologies.
And that’s not to mention the many benefits machine learning offers to organizations across industries. But let’s take it one step at a time.
Understanding Machine Learning: What It Is and How It Works
Solutions created using machine learning coding languages are the key to tackling tasks that were once too complex or even impossible. ML has become a driving force for progress across all fields—from medicine and transportation to finance and education.
For example, traditional lung cancer diagnostics rely on analyzing X-ray images. Doctors require a significant amount of time for examination, and even they can make mistakes (since the human eye can’t always detect what a program might spot).
An ML-powered solution: a specialist, using a language for machine learning, trains an AI-based program to recognize early signs of the disease. As a result, the model can:
- Analyze thousands of images in just minutes;
- Identify suspicious areas with high precision;
- Reduce the likelihood of human error.
The outcome? Faster and more reliable diagnostics, along with the ability to detect diseases at the earliest possible stages—ultimately saving lives.
Using programming languages for machine learning, you can achieve truly fantastic results! In agriculture, ML systems analyze soil conditions and predict crop yields. In finance, ML is used to detect credit card fraud. In transportation, ML analyzes millions of real-time data points from sensors to prevent accidents (like Tesla’s autopilot).
How does it work? Machine learning programming languages are used to teach a computer to solve tasks independently, much like how humans learn from experience.
- First, a large volume of data is collected for the computer to learn from. For example, to train a facial recognition system, thousands of photos of different people can be used.
- The data is cleaned, structured, and formatted. For instance, photos may be cropped, resized, and converted into numerical values.
- A specialist selects an appropriate machine learning language and algorithm. There are various types of algorithms, each suited to specific tasks. For example, logistic regression can be used for classification (determining the class of an object), while the k-means algorithm is suitable for clustering (grouping data).
- The algorithm is applied to the data, and the computer begins learning from it. During this process, the model identifies patterns in the data and creates a mathematical representation to describe these patterns.
Fun fact! You don’t need to know the best machine learning language to contribute to AI training. In fact, all of us have been involved in this process in one way or another. Every time we successfully completed a CAPTCHA, we were essentially providing training data for neural networks. In this way, millions of people worldwide, by solving CAPTCHAs, have helped train computer vision algorithms.
Why Machine Learning Offers Significant Advantages for Businesses
Machine learning is important not only for IT companies but for any business, as it can solve universal tasks, boost efficiency, and unlock new opportunities in virtually any industry.
There are many reasons to learn a language used for machine learning. Let’s look at the key ones.
1. Informed decision-making. Today, data is generated everywhere—from social media to IoT sensors. It’s impossible for humans to process this volume manually. ML can analyze millions of rows of data in seconds, uncovering hidden patterns. This enables businesses to make more fact-based decisions rather than relying on intuition.
2. Increased efficiency. ML can automate routine tasks, freeing employees for more creative work. For instance, a programmer using languages used in machine learning can develop a chatbot capable of serving customers with minimal involvement from the sales department.
3. Forecasting and analytics. Traditional methods of analysis and prediction can’t keep up with the pace of change. ML enables real-time adaptation by studying data changes as they occur.
4. Enhanced customer experience. Machine learning makes it possible to create personalized offers and recommendations for customers, boosting their loyalty. This is achieved by analyzing past purchases, search history, viewed products, wish lists, and more.
5. Solving complex tasks. For instance, image recognition, disease diagnosis, or market price forecasting—all require complex computations and precision. ML can handle these challenges due to its ability to uncover hidden relationships in data.
6. Creating new products and services. If your IT department is using languages for machine learning and integrating AI functions into internal programs, your company already has a significant competitive edge. At the very least, this opens up opportunities for developing innovative products and services, such as recommendation systems, image recognition, and natural language processing.
With a specialist who knows a language machine learning , you can tackle nearly any problem! Or, at the very least, break it down into sub-tasks that can be solved with ML. The key is to have enough data to use for training.
Top Programming Languages for AI and Machine Learning
What language is used in machine learning? Phew, there are quite a few!
The oldest language used in ML is Lisp. It was created in 1958 and was used for artificial intelligence research. However, it is rarely used in modern times.
The newest machine learning programming language is Julia. Released in 2012, it quickly gained popularity in scientific and computational tasks thanks to its unique capabilities.
The variety of languages is due to their historical development, tasks, and the features they offer.
- R was developed for statistics and data analysis.
- Python became universal due to its ease of use and powerful ecosystem of libraries.
- C++ is used for high performance.
Why the Right Programming Language Matters in Machine Learning
Choosing the best programming language for machine learning is critically important, as it can significantly impact the success of your project.
Different languages offer different levels of performance. If you're working with large datasets or complex models, the speed of code execution could become a decisive factor.
Moreover, each of the machine learning languages comes with its own set of libraries and tools. It’s important to pay attention to the richness of the ecosystem, as this will simplify your work and reduce development time.
Depending on your infrastructure and other tools in use, you may need a language that easily integrates with other systems.
In other words, you should consider the complexity of the task, the team's experience, available resources, and project timelines. There’s no universal answer that fits every case of machine learning. Best programming language is the one that will give you the best result.
Exploring the Best Coding Languages for Machine Learning Applications
So, what is the best language for machine learning? There is no single "best" language—it all depends on your tasks. However, Python has become the de facto standard for machine learning due to its simplicity, large community, and extensive ecosystem of libraries. But there are also other interesting options.
Python: The Leading Language for Machine Learning Projects
Python is extremely popular due to its simplicity, rich ecosystem of libraries (such as TensorFlow, PyTorch, and Scikit-learn), and an active community. It is ideal for rapid prototyping and developing complex models. Python also offers a wide range of ready-made models and solutions, from visualization to computations.
"I love Python for its simple and readable syntax, excellent documentation, and large community. For me, integration with Jupyter Notebooks for interactive development is also very important. As for drawbacks, I might mention issues with multithreading due to the GIL. Some also say Python has relatively slow execution speed, but personally, I don't find this critical."
© Senior developer of WEZOM
R Programming Language: A Powerful Tool for Data Science and ML
A specialized programming language for machine learning and statistical processing. It is great for research tasks and visualization. Libraries: caret, mlr, randomForest, ggplot2.
Advantages:
- Excellent data visualization (ggplot2)
- A large number of specialized ML packages
- A strong academic community
If we compare Python vs R for machine learning, R has a much narrower specialization and a significantly steeper learning curve compared to Python.
Julia: A Rising Star in the Machine Learning Community
A relatively new programming language for machine learning. It was specifically designed for scientific computing. Libraries: Flux.jl, MLJ.jl, Turing.jl.
Advantages:
- High performance, comparable to C++
- Simple syntax, similar to Python
- Built-in support for parallel computing
- Direct integration with C/C++ libraries
The downside is its young ecosystem (which results in fewer ready-made solutions and libraries for Julia).
Java in Machine Learning: Reliable and Scalable Solutions
One of the very stable programming languages: machine learning and industry — Java is great for large-scale systems.
Advantages:
- Excellent performance
- Strong typing and reliability
- Good scalability
- A large number of enterprise tools
If you decide to purchase books on AI programming, you’ll find not much information on Java. This language has more verbose code, which is why many developers don’t like it. Another downside is the relatively small number of specialized ML libraries.
Lisp for Machine Learning: A Legacy Language with AI Power
You definitely won't find it among the top machine learning languages. However, on the other hand, Lisp is a legend. It supports symbolic data processing, has powerful metaprogramming capabilities, and a flexible macro system.
Disadvantages: very small modern community, few modern ML libraries, complex machine learning development tools.
JavaScript and Its Role in Machine Learning Development
In all JavaScript for machine learning tutorials, you will find almost the same information:
- Allows running ML models in the browser
- Easily integrates with web applications
- Cross-platform
There are a few downsides as well: limited capabilities for complex computations, few libraries for advanced analysis
C++ in Machine Learning: Efficiency at Its Best
Used for creating high-performance ML systems. Provides full control for optimizing computations. Suitable for tasks with intensive calculations. Also used for developing libraries (for example, TensorFlow and PyTorch are partially written in C++).
Advantages:
- Maximum performance
- Direct access to hardware
- Ability for fine optimization
- Used in the core of many ML frameworks
You can read all the machine learning language learning resources, but you might not find a clear answer about the main drawbacks of C++. Some say it's the complex syntax, others point to the long development time. But overall, nothing critically important that would drastically affect your decision.
Go (Golang) for Deploying and Serving Models in Production
Go (Golang), developed by Google, is widely recognized for its efficiency, simplicity, and reliability. While Python has long dominated the machine learning domain, Go offers unique benefits that make it an attractive option for specific use cases:
- High execution speed: Compiled directly into machine code, ensuring fast performance.
- No external dependencies: Simplifies deployment by avoiding reliance on external packages.
- Strong REST API support: Ideal for building scalable web services.
- Ease of creating microservices: Simplifies the development and maintenance of distributed systems.
Limitations: smaller ecosystem of ML libraries. Lack of native matrix and vector processing support: These features need additional libraries. Many advanced algorithms have no direct implementation in Go.
Choosing the Optimal Language for Your Machine Learning Project
As we mentioned earlier, the choice of language depends on your tasks. If you're dealing with data analysis, forecasting, and model creation, Python will cover almost all your needs. If you're developing interactive web applications with ML models, JavaScript might be required. If you need high-performance solutions, it's worth studying C++ or Java.
Let's conduct a quick machine learning languages comparison.
The Future of AI: How Programming Languages Shape Machine Learning
The future of popular programming languages in AI, like the entire field itself, is closely tied to the emergence of new, more specialized technologies. Tools will develop that allow for the automation of many routine tasks in the process of building machine learning models.
We are confident that languages will increasingly integrate with cloud platforms, providing developers with convenient tools for creating and deploying models. Additionally, there will be a stronger focus on the interpretation of the data being produced.
One thing we have no doubt about is that the AI market size will grow at an enormous pace. Currently, it stands at 243.72 billion U.S. dollars, and in just three years, this number is expected to more than double, reaching 529.23 billion U.S. dollars.
The accelerating pace of change is like a snowball avalanche that will overwhelm anyone who hasn't taken preventive measures. Businesses that fail to start learning top programming languages for AI and implementing best practices will already have lost the competitive race.
FAQ
What Languages Are Commonly Used for Machine Learning?
Best languages for machine learning include Python, R, C++, Java, Julia, and JavaScript. Each has its own characteristics, and the choice should depend on your project's specific tasks, taking into account development time, integration needs, team expertise, and the application area.
Which Programming Language Reigns Supreme in Machine Learning?
Python is undoubtedly one of the best coding languages for AI. Its simplicity, vast library ecosystem, and active community make it the ideal choice for most projects.
Is Python or C++ Better for Building Machine Learning Models?
The choice between Python and C++ is nuanced. For training and development, Python is generally the better option due to its ease of use and abundance of libraries. For tasks that demand high performance and optimization, C++ is the more suitable choice.
Which Is the Best Option: R or Python for Machine Learning?
If the task is focused on statistics and visualization, R is the better option. For a more versatile approach to machine learning, Python is generally preferred due to its wide range of libraries and flexibility.
Java vs. C++: Which Is More Suitable for AI Development?
For enterprise applications, Java is a more suitable choice. For high-performance systems or creating libraries, C++ is the better option due to its optimization capabilities.
Can Multiple Languages Be Combined in a Single Machine Learning Project?
Yes, combining multiple languages in a single machine learning project is common practice. Integration can be done through libraries (e.g., calling C++ from Python via ctypes) or via APIs (e.g., REST or gRPC) to enable communication between components in different languages.