Top programming languages for machine learning 

Machine learning (ML) as a sphere of Artificial intelligence (AI) is not a new concept in computer science; it’s been gaining popularity since the beginning of the big data use. Now, it’s reflected almost everywhere — in the way social media generate page recommendations based on our likes and interactions, the way online shops offer goods and services related to our previous purchases, and how media offer subscriptions according to our previous views, purchases, and fields of interest. 

A rapid development and variety of application fields open countless possibilities for businesses to optimize their processes and offer more client-oriented services. For software developers, it raises the question of which programming language to choose for ML to create a program that will, like a human being, respond to specific requirements. With this blog, we shed a light on ML, it’s application across industries, and help you define what is the best programming language for machine learning. 

What is machine learning? 

Let’s start with defining learning itself — the ability to adjust behaviour based on collected information. Conventionally, it’s been a characteristic of humans and animals, however, now, when we unlock our phones with face ID or interact with a chat bot, we get a confirmation that machines, too, are capable of learning. 

The simplest definition of Machine Learning (ML), therefore, will sound like a form of artificial intelligence that can learn without explicit human programming. The term appeared in 1959 when American computer scientist — Arthur Samuel — defined it as ‘a computer’s ability to learn without being explicitly programmed.’  

To do so, machine learning algorithms detect patterns and learn how to make predictions and recommendations by processing data and experiences rather than receiving programming instruction. The algorithms constantly collect data and adapt to improve and become more precise over time. 

When do we use it? 

Machine learning is find application in nearly every industry and business activity. To name a few, the logistics industry benefits from optimized shipping and delivery routes, retail — from personalized shopping experiences, manufacturers — from automated factories. Casual everyday example is using our voice to query a smartphone or speaker (Hey, Siri!). Machine learning helps to recognize, analyze, and understand the request and find the best matching result. Today we can confidently say that the scope of ML use cases is nearly endless.  

The market size is expected to show an annual growth rate (CAGR 2025-2030) of 34.80%, resulting in a market volume of US$503.40bn by 2030. (Source: Statista) 

Machine learning benefits across industries 

Enhanced decision-making 

ML algorithms process and analyze vast amounts of data and spot patterns, present structured conclusions and insights, and help, therefore, make more informed decisions. Organizations get more visibility and can, therefore, make data-driven choices, optimize their processes, and scale, relying on knowledge instead of guessing. 

Automation and efficiency  

Machine Learning can handle repetitive tasks and complex processes, improving overall efficiency and productivity and being an incredible time saver. It also frees valuable human resources whose expertise and knowledge can be applied to managing more vital and high-value processes. 

Personalised experiences  

As mentioned earlier, generative AI and ML gift organizations with the possibility of offering more personalized user experiences, not only winning the audience but increasing loyalty. By analyzing user performances, behaviour, and historical data, businesses customize recommendations and marketing campaigns. 

Fraud detection and security 

One of the key applications of ML is using its algorithms and patterns to indicate anomalies, fraudulent activities, and security breaches. Maintaining the highest level of security is vital for every industry, and ML helps to protect sensitive data and enhance cybersecurity measures. 

Predictive analytics 

ML models can make predictions based on data they have obtained and analyzed. This way, organizations can define trends and demands and allocate resources according to these predictions. These predictions also impact business decision-making, improving overall efficiency and saving resources.  

Improved customer service 

The best possible customer experience is personalized — based on their needs, expectations, and previous activities. ML can automate customer service interactions to better understand customer queries and provide more relevant and informative responses. This saves time and resources and improves customer loyalty and satisfaction. 

Cost savings  

Saving resources is fair in all spheres of ML application. Various industries can achieve cost optimization by automating processes, reducing the number of errors, making more informative, data-based decisions, and more effectively allocating human resources. 

Why is machine learning language selection important? 

Each programming language has strengths and weaknesses that affect its suitability for a project. Some languages might be well designed to handle the types of tasks the project involves or support necessary extensions through libraries, frameworks, and tools. Some compile code into machine language more efficiently than others, accelerating delivery and lowering memory usage. Some may be more compatible with target OS-es or hardware environments. This is why there are so many — over time, developers create and refine languages to achieve better outcomes, simplify critical tasks, and introduce new capabilities. 

ML and AI bring unique demands, including extensive data manipulation, strong I/O, and substantial mathematical calculations. Although most modern programming languages can support these needs to some extent, project managers and development teams must select the best one for the project’s needs. A badly chosen language is an extreme disadvantage regarding cost-effectiveness, performance, security or reliability. 

Top machine-learning languages 

Python  

The main reason why Python is often called the best programming language for machine learning is its simplicity and readability. This enables developers, even beginners, to focus on the problem-solving aspects of AI and ML rather than dealing with the complexity of the language syntax. 

Python also provides a robust standard library, reducing the need to code everything from scratch. However, the main point in favour is Python’s rich ecosystem of specialized libraries for AI and ML. Libraries such as NumPy, pandas, and Matplotlib take care of the numerical aspects and data visualization, while SciPy brings additional scientific computing capabilities. Libraries such as Scikit-learn offer a variety of algorithms and utilities for regression, classification, clustering, and more. TensorFlow and PyTorch are top-notch libraries for creating and training advanced machine learning models, including deep learning models. 

Another strong suit of Python is its interoperability. It can easily communicate with other languages like C and C++, enhancing performance. Moreover, Python’s extensive community support ensures that developers can quickly find solutions and get help when needed. 

R  

R is a favourite choice among statisticians and data scientists for conducting exploratory data analysis, statistical tests, and model fitting. Its comprehensive suite of statistical and graphical techniques includes all varieties of regression, classical statistical tests, time-series analysis, classification, clustering, and much more.  

R is also great for data visualization, with packages like ggplot2 offering a range of aesthetically pleasing graphs and plots. This ability to represent data intuitively is an integral part of data analysis, making R an effective tool for understanding complex data. 

Moreover, R’s data handling capabilities are impressive, compared to other machine learning programming languages. It can handle larger datasets than many other programming languages, including Python, which is a definite advantage when working with big data. 

R also boasts a vibrant, active community. This means a rich ecosystem of user-created packages that extend the language’s capabilities, plus an abundance of resources for learning and troubleshooting. 

However, R isn’t without its shortcomings. The most common criticism is its relatively difficult learning process, particularly for those not familiar with programming. Its syntax is unique and can be confusing for beginners. 

Another downside is that R can be slower than other languages, such as Python or C++, when it comes to executing complex algorithms, which might be a concern for time-sensitive applications. Additionally, R might not be the best choice for general-purpose programming or application development outside the data analysis and statistics domain. 

C++  

Speed and efficiency are the key attributes of C++. It provides a level of control over system resources that few other languages can match. With C++, developers have direct control over memory management, allowing for fine-tuning that can lead to significantly improved performance. This makes it an excellent choice for resource-intensive AI and ML tasks, including real-time applications. 

Moreover, C++ excels in situations where multithreading and concurrency are required, and its ability to run close to the system hardware makes it suitable for implementing game AI, robotics, real-time systems, and more. 

That being said, C++ isn’t all sunshine and rainbows. It has a steeper learning curve than other languages like Python and R, which can deter beginners. While powerful, its syntax is more complex and less readable, requiring a solid understanding of programming concepts. Furthermore, C++ lacks the extensive library support for AI and ML seen in Python, which can make implementation more time-consuming. 

Popular C++ libraries do exist for AI and ML, though. For instance, MLpack is a machine learning library in C++ that emphasizes speed and flexibility. Similarly, Shark provides algorithms for large-scale optimization. Libraries like Dlib, known for their real-time processing capabilities, and Tensorflow, which has C++ API in addition to its primary Python interface, are also available for use. 

Is C++ good for AI? While C++ may not be the first choice for beginners in the realm of AI and ML due to its complexity, it provides unmatched speed and efficiency that can be critical for certain resource-intensive applications. With a well-thought-out approach, and by taking advantage of the relevant libraries, using C++ for AI and ML can be a powerful choice for the right projects. 

Java  

Java offers a host of features that make it a suitable language for AI and ML. Its strong typing system helps prevent errors, leading to robust code, while its object-oriented programming (OOP) model promotes modularity and reusability, both critical for large-scale AI/ML projects. 

The performance of Java is another strength, with just-in-time compilation offering speed close to lower-level languages like C++. Its extensive standard library provides functionality for a broad range of tasks without requiring external packages. 

Java’s platform independence, captured in the phrase “Write Once, Run Anywhere,” makes it highly portable. This feature can be beneficial in AI/ML projects that need to be deployed across different operating systems. 

Moreover, Java’s extensive tooling ecosystem, including integrated development environments (IDEs) like Eclipse and IntelliJ IDEA, testing frameworks, and profiling tools, can increase productivity and make the development process smoother. 

On the flip side, Java can be more verbose and complex than some other languages like Python, which can slow down development and make the code harder to understand. Furthermore, while Java does have libraries for AI/ML, it might not offer the same level of ease of use or variety as Python or R. 

JavaScript  

JS is not the most obvious choice when it comes to defining what language is best for machine learning, but its ubiquity brings it to this list. As the principal language for web development, JavaScript runs in all modern browsers, making it accessible to a wide array of developers and allowing for effortless deployment of AI and ML models on the web. 

Furthermore, JavaScript’s event-driven, non-blocking I/O model makes it well-suited to real-time applications such as chatbots, personal assistants, or any application that requires real-time user interaction. 

However, JavaScript is not without its challenges when it comes to AI and ML. It is not as efficient or fast as languages like C++ or Python, and it was not designed with mathematical computations in mind, which are core to AI and ML algorithms. Also, while it is improving, JavaScript’s library ecosystem for AI and ML is not as mature as that of Python. 

Nevertheless, various notable JavaScript libraries are available for AI and ML. TensorFlow.js, a JavaScript library for training and deploying machine learning models in the browser and on Node.js, stands out prominently. Synaptic is another comprehensive library for building neural networks. For natural language processing, there’s compromise, and for machine learning, there’s machinelearn.js, amongst others. 

Bottom line 

Choosing the right machine learning language is the key step since it defines the performance and scalability of the final solution. We already know that Python is the dominant one among the best languages for machine learning because of its simplicity along with a rich ecosystem. R is great for vizualization and statistical analysis. C++ stands out with its speed and control. Java comes with an incredible stability and cross-platform compatibility. And last but not least, there is a less conventional but solid option  for web-based machine learning applications — Java Script.  

Which one to opt for depends on the project requirements and complexity, the amount of resources, urgency, and deployment environment. Sounds like a tough choice? Contact us and we will define the best language for machine learning for your prosperous tech solution.