Machine learning is booming, with its global market expected to grow from $26 billion in 2023 to over $225 billion by 2030. That’s a ton of ML models still in the pipeline. Besides, consider this: the demand for AI and ML specialists is set to increase by 40%, or a million jobs by 2027.
Given such rapid growth, choosing the best programming languages for machine learning is more crucial than ever. Here’s why. Different languages have different strengths— Python’s simplicity, R’s statistical analysis, and Julia’s performance. The right technology can make building your ML project way faster, more accurate, and, well, just plain easier.
So, keep reading because we’re about to study the top machine learning languages and show how they can impact your project’s success.
What Is AI and ML?
Before we explore the machine learning programming languages in all the details, let’s first figure out AI and ML.
Artificial Intelligence (AI) is the field of computer science that deals with creating machines capable of mimicking human cognitive functions like learning, reasoning, and problem-solving. Think chess-playing computers or chatbots that can hold conversations.
Machine Learning (ML), on the other hand, is a branch of AI that focuses on training computers to learn and improve from data. Unlike traditional programming, where developers write explicit instructions for every task, ML systems study data and make independent decisions based on it. Just like that app on your phone that identifies duplicate pictures to free up storage space — ML algorithms work behind the scenes.
Thus, while AI combines a wide range of technologies to create intelligent systems, ML specifically focuses on using data to train these very systems.
Why AI and ML Matter?
Artificial intelligence and machine learning have already changed how we live and work. In fact, Stanford’s study found that AI beats humans on some tasks: English understanding, visual reasoning, and image classification, to name a few. But these technologies are way more impactful, offering:
Predictive analytics. AI and ML can predict future trends and outcomes based on historical data. Think about using machine learning in predictive maintenance to anticipate equipment failures before they actually happen or forecasting market fluctuations to make wise investment decisions.
Automation. Repetitive, mundane tasks are no longer a problem with ML and AI. These technologies can be used to automate processes, improve operational efficiency, and free up time for more strategic or creative activities.
Personalization. Ever wondered how your streaming service always seems to recommend shows you’ll love? That’s thanks to AI and ML. With these techs in place, you can study user preferences, behaviors, and other relevant data and create truly personalized experiences.
Improved decision-making. From fraud detection in finance to optimizing traffic flow in cities, AI and ML help us make smarter decisions. These technologies analyze large datasets — far larger than what humans can handle manually — and uncover hidden insights and patterns.
Criteria for Evaluating Programming Languages for Machine Learning
To optimize your development process and achieve the desired outcomes, you need to select the right language. Let’s go through some key criteria when evaluating programming languages for ML:
Ease of use and learning curve. How easy is it to pick up and use the language? Language with clear syntax, readily available tutorials, and a gentle learning curve are ideal for beginners. On the other hand, more complex technologies can provide more control and performance benefits for advanced users.
Performance and speed. Machine learning often involves processing large datasets and complex algorithms. In this case, the language’s efficiency is critical. Consider the command execution speed and CPU and memory usage when making your choice.
Libraries and frameworks. These tools provide pre-built functions and pre-written code that save the dev’s time and effort when implementing algorithms and manipulating data. Prioritize languages that offer substantial library and framework support.
Community support and resources. A large and active community around a programming language is invaluable. Online forums, tutorials, and documentation can help developers in case questions or issues arise.
Integration with other tools and systems. Machine learning projects typically interact with various tools and systems. Consider how well your chosen language integrates with databases, cloud platforms, visualization tools, and other solutions.
Top 10 Programming Languages for Machine Learning
When it comes to machine learning projects, several programming languages have gained prominence. Here are the best ten options you can use for ML:
Python
Python is a dominant language in artificial intelligence and machine learning. And it’s easy to see why:
Readability and simplicity. Python’s clean, readable syntax makes it easy to learn and understand. It lets developers, even beginners, focus more on problem-solving and less on complex syntax.
Community support. Due to Python's active community, developers can seek help, share knowledge, and collaborate on projects easily.
Interoperability. Python can be used alongside C and C++ languages, which makes it perfect for computationally intensive tasks.
Vast ecosystem. Python has an extensive collection of libraries and frameworks specifically for AI and ML — think NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.
Let’s discuss the above libraries in greater detail.
NumPy. Short for Numerical Python, this library is responsible for numerical operations on large arrays and matrices.
Pandas. This one offers data structures and tools for data manipulation and analysis.
Matplotlib. It helps create static, animated, and interactive data visualizations.
Scikit-learn. Built on NumPy and SciPy, this library provides classification, regression, clustering, and other algorithms.
TensorFlow. An open-source platform for building and deploying ML models, particularly for deep learning.
PyTorch. It’s an ML library used for natural language processing and computer vision.
Despite the numerous advantages, Python comes with certain limitations. It may perform slower than C++ or Java and isn’t suitable for mobile application development.
R
R is a strong player in data science and machine learning. Here’s why it stands out:
Statistical analysis. R is made for statistical computing, exploratory data analysis, and hypothesis testing, featuring a wide range of statistical functions.
Data visualization. R lets you easily visualize data, particularly through tools like ggplot2.
Data manipulation. R can handle complex datasets, which is beneficial when working with big data.
While R is undeniably powerful, it can be slower than Python and C++. Besides that, R’s unique syntax can be difficult to learn for those without a statistical or programming background.
The libraries you can use when creating ML projects in R include the following:
Caret. It’s a package for training and evaluating machine learning models.
RandomForest. As the name suggests, this package implements random forest algorithms for classification and regression tasks.
E1071. This package includes functions for Support Vector Machines and other ML algorithms.
Ggplot2. It’s used to create informative data visualizations.
Java
Java is a widely used, versatile, and robust language, especially suitable for large-scale, enterprise-level applications. Its core strengths are:
Performance and scalability. Java’s efficient memory management, modular nature, and just-in-time compilation make it perfect for computationally intensive tasks and large datasets.
Cross-platform compatibility. Java’s “Write Once, Run Anywhere” principle allows for the deployment of AI models across different platforms.
Mature ecosystem. As a long-standing language, Java has plenty of IDEs, testing frameworks, libraries, and profiling tools to offer.
Speaking of the libraries and tools, the most popular ones include:
Weka. A collection of ML algorithms for data mining tasks.
Deeplearning4j. A distributed deep learning library for Java.
Mallet. Short for Machine Learning for Language Toolkit, it’s a package for NLP, cluster analysis, topic modeling, and other ML applications.
Despite being time-tested and performant, Java may appear more complex than, for example, Python. And while it does offer machine learning libraries, the variety available may not be as extensive as in other languages.
Julia
Julia, while relatively new to developers, is a rising star in scientific computing and machine learning communities. Why? There are several reasons for this:
Speed and performance. Julia is designed for high performance, combining Python’s ease of use and speeds comparable to low-level languages like C++.
Scientific computing focus. Julia’s syntax and standard library are well-suited for numerical computations, making it a logical fit for scientific applications.
Hybrid approach. Julia combines the best aspects of both Python and C and lets you use Python and C tools with it.
On the flip side, Julia’s ecosystem is still smaller compared to more mature languages. Here are the libraries available:
Flux.jl. It’s a flexible library for machine learning.
Knet.jl. It’s another ML library similar to Flux but focusing on low-level control and optimization.
MLJ.jl. This is a general-purpose machine learning interface that unifies various ML algorithms and models.
Scala
Scala, a blend of object-oriented and functional programming, is a decent choice for AI and machine learning. Here’s why:
Scalability. Scala, literally derived from Scalable Language, can easily handle large datasets and distributed computations.
Concise and expressive. Scala’s syntax combines the best of object-oriented and functional programming, which makes code clean and efficient.
Interoperability with Java. You can use Java’s libraries and tools within Scala.
The main drawback of starting with Scala is its steep learning curve, as it requires mastering both object-oriented and functional programming simultaneously.
Here are the main libraries you may use with Scala:
Spark MLlib. Built on top of Apache Spark, MLlib offers numerous machine learning algorithms and utilities.
Breeze. A numerical computing library for Scala, which provides linear algebra and optimization routines.
Deeplearning4j. A deep learning library that can be used with Scala.
Lisp
Lisp, one of the oldest programming languages, has a rich history in artificial intelligence. But is it still relevant for modern AI and ML applications? Let’s see.
Symbolic manipulation. Lisp’s support for symbolic computing makes it suitable for knowledge representation and reasoning tasks.
Flexibility and extensibility. Despite its old age, Lisp is highly flexible. Its metaprogramming capabilities allow for the creation of domain-specific languages.
Dynamic typing. Lisp’s dynamic typing lets you experiment with ML algorithms and handle rapid prototyping.
Unfortunately, Lisp has a smaller community than more mainstream languages, meaning fewer resources and libraries are available.
C++
C++ is a low-level, high-performance language perfect for computationally intensive AI and ML tasks. Here are some of its stand-out features:
Performance. C++ offers unmatched speed and efficiency, crucial for large-scale data processing and complex ML models.
Control. It provides precise control over memory management and hardware, optimizing resource usage.
Vast ecosystem. While smaller than Python’s one, C++ has an extensive ecosystem of libraries and frameworks for machine learning.
Popular options include:
Caffe. It’s a deep learning framework that offers a C++ library for building and training neural networks.
OpenCV. This is a library for computer vision.
Dlib. A C++ toolkit that includes machine learning algorithms and tools for computer vision.
Shark. It’s an open-source library that offers optimization, clustering, and classification algorithms.
While undoubtedly performant, C++ has a steeper learning curve than all the languages we’ve listed. Because of its complexity, custom development can be more time-consuming.
JavaScript
JavaScript, primarily known for web development, has expanded its capabilities to AI and machine learning. While not a traditional choice, it offers unique advantages:
Browser-based AI. JavaScript lets AI models run directly in web browsers, creating interactive and real-time applications.
Event-driven. JavaScript is perfect for real-time applications like chatbots or personal assistants.
Community and ecosystem. Being the #1 choice for developers, JavaScript offers a large community with extensive resources, tutorials, and support.
Some of the ML-centered libraries and tools to use are:
TensorFlow.js. It’s a JavaScript library for training and deploying machine learning models in the browser and on Node.js.
Brain.js. This is a library for neural networks in JavaScript.
Ml5.js. It’s a library that offers simple and accessible machine learning tools for web development.
With all the advantages listed, the drawback of JavaScript is its limited performance in computationally intensive tasks.
Prolog
Prolog, a declarative language built on logic, has been a cornerstone in AI since its inception. Here’s how it transforms machine learning:
Logic-based reasoning. Prolog is perfect for tasks that require knowledge representation and problem-solving through logical deduction.
Natural language processing. Prolog’s pattern-matching capabilities work best for natural language understanding and processing.
Declarative approach. In Prolog, you define what you want to achieve rather than how to achieve it. It lets you specify problems to be solved accurately.
Still, Prolog is a niche language, which limits the library and community support. It can also perform slower than, for example, C++ or Java.
Haskell
Haskell, a purely functional language, is an unconventional approach to programming with an emphasis on mathematical rigor and correctness. Here’s why it can work for AI and ML:
Functional programming. Haskell’s functional nature promotes code clarity, modularity, and correctness, which is necessary for complex AI algorithms.
Strong type system. Haskell’s static type system helps catch errors at compile time, ensuring code reliability.
However, the Haskell community is relatively small, and the language itself is difficult to learn. Despite that, you can still leverage the following AI and ML libraries:
HLearn. A machine learning library for hom*omorphic learning.
HMatrix. It’s a numerical computing library for Haskell.
TensorFlow-Haskell. This one provides bindings to the TensorFlow library.
Comparing the Most Popular ML Languages
Having discussed the best programming languages for machine learning in-depth, let’s recap. Here’s a comparison table with key parameters:
Conclusion
Machine learning is a thriving domain. Yet, it’s impossible to guide through it without choosing the right programming language. From statistical prowess and syntax simplicity to logical thinking and high performance — pick what suits your desired outcomes.
Need help selecting machine learning programming languages for your project? As an expert with 200+ AI solutions delivered, Integrio can provide expert advice. Contact us anytime and get a detailed consultation.
