What's the best programming language for machine learning?

Why is programming language selection important?

Every programming language is built with a unique set of commands, syntax and semantics used to compose instructions. That composition -- the code -- is then compiled into machine language that can be executed by the computer's CPU and other processors, such as GPUs, neural processing units, tensor processing units and other semiconductor devices optimized for ML and AI tasks.

Each language has strengths and weaknesses that affect its suitability for a given project. A language might be well designed to handle the types of tasks the project involves, for example, or support necessary extensions through libraries, frameworks and tools. Similarly, some languages compile code into machine language more efficiently than others, leading to faster execution and lower memory usage, and certain languages may be more compatible with target OSes or hardware environments.

This is why so many different programming languages exist. It's a process of natural selection and evolution applied to technology: Over time, developers create and refine languages to achieve better outcomes, simplify critical programming tasks and introduce new capabilities. New languages are eventually born, while older ones find a niche or become obsolete.

ML and AI impose unique demands, including extensive data manipulation, strong I/O (for moving large amounts of data to and from storage) 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 language for each project's particular needs. Choosing a suboptimal programming language may leave an ML or AI project at a competitive disadvantage in terms of cost effectiveness, performance, security or reliability.

Language selection factors for ML and AI programming

Selecting a language for ML and AI programming projects involves many of the same considerations as other types of programming. While no language is perfect for every situation, key factors to consider include the following:

• Code syntax and semantics. A language's command set, syntax and semantics have a profound impact on how code is written and maintained. The ideal language will result in clear and concise code that is effective, easy to follow, supports established code quality standards and reduces common errors.
• Code elasticity. Elasticity refers to the ease with which developers can change and improve code. Languages that support readable, concise code with fewer complex routines enable faster and simpler updates. For example, a language that requires 50 lines of code to accomplish a task is less elastic than one that only needs five.
• Tooling and support. A programming language never exists in a vacuum; it requires accompanying tools, such as an integrated development environment, libraries and frameworks. Mature languages typically have extensive tooling and community support, which gives developers more options and can accelerate project development.
• Code performance. Performance measures how efficiently code runs in the target environment. The right language usually results in smaller executables, faster execution, lower resource consumption and better portability to different environments. In ML, performance affects scalability, training time and corresponding costs, such as cloud compute. But language choice is just one factor -- architecture and implementation decisions also influence performance.
• Code scope. This consideration involves the code's compatibility and interoperability. Code compatibility defines how well a language creates code that can function on different OSes or target hardware environments, whereas interoperability refers to its ability to exchange data with other software or hardware systems. Given that ML and AI often involve handling large, varied data sets, interoperability is usually more important than compatibility.
• Staff experience. Don't overlook the value of in-house expertise. Even if an emerging language seems ideal for a certain project, using a more familiar option that teams know well could help deliver faster, better code at a lower cost. If the necessary expertise isn't available internally, the business might need to hire new talent or outsource the work.
• Language popularity. Language popularity affects the availability of skilled developers, libraries, and support from the language creators and programming community. Even if a language is ideal from a technical perspective, it may not be the best choice if a business can't find experienced practitioners or community support.

Best programming languages for ML and AI

Although software developers can choose from hundreds of programming languages, several major languages stand out for ML and AI programming projects.

Python

Python is likely the most popular language for ML, AI and data analytics. It's a high-level, general-purpose language, which makes it slower to execute than languages like C++. However, this is more than offset by its simplicity and versatility. Python is easy to learn, read and maintain, making it ideal for quick prototyping. It's widely used for sentiment analysis and natural language processing and is supported by extensive libraries and frameworks. These include PyTorch, TensorFlow and Keras for deep learning, scikit-learn for ML algorithms, NumPy and pandas for data science, and the Natural Language Toolkit for language data.

C++

C++ is a well-proven and popular object-oriented language. It is a low-level language, meaning its code is granular and relatively close to machine language. While this improves efficiency, it also means that developers must invest significant effort to write and maintain code in C++. Properly architected C++ programs typically compile and run with excellent performance and lower resource usage, making it a great choice for performance-critical ML tasks. ML and AI C++ libraries include Caffe for deep learning, DyNet for neural networks and Shogun for general ML.

Java

Java is a versatile, object-oriented language with moderately complex syntax, although not as low level as languages such as C++. Java is known for strong performance and portability, enabled by Java Virtual Machines (JVMs). Java is highly scalable and well suited for large ML algorithms using frameworks like Hadoop, Hive or Spark. Java's ML libraries include Weka for data analytics and predictive modeling and the Massive Online Analysis framework for tasks like classification, regression and clustering.

R

R is a function-centric language popular in data science for tasks involving data analytics and visualization. Although R has added object-oriented programming capabilities, it is more often used in specific ML or AI modules designed to handle heavy math and statistics, leaving tasks such as APIs and the UI to other languages. R is supported by thousands of ML and AI extensions, including caret for predictive modeling, RandomForest for random forest algorithms and Plotly for data visualization.

Julia

Julia is a high-level, open source language designed for scientific computing, including complex linear algebra and mathematical simulations. It combines ease of learning with excellent performance thanks to just-in-time compilation. It's ideal for ML and AI tasks that require numerical accuracy and involve high levels of complexity, and its support for distributed computing and parallelism are useful for deployment in cloud environments. The Julia ecosystem offers many ML libraries and frameworks, including Flux.jl for general ML, JuliaStats for statistical modeling and data analytics, and DifferentialEquations.jl for advanced math tasks.

Go

Go, a compiled, high-level language developed by Google, is known for its simplicity and support for concurrency, making it well suited for parallel and distributed processing. Go's memory safety and garbage collection features, along with its ability to handle large data sets, make it a strong choice for ML and AI. Go is particularly effective for building modular components for microservices, which can be combined for sophisticated ML workflows. While Go lacks the extensive ecosystem of more mature languages, it has some support from powerful ML libraries such as TensorFlow and GoLearn.

Haskell

Haskell is a functional programming language valued for its mathematical accuracy and reliable, concise and often immutable code. Its emphasis on function-based programming reduces bugs and runtime errors, making it stable and attractive for AI education and production environments. ML libraries and tools for Haskell include HLearn for ML algorithms and tasks; NumPy-like for advanced mathematics; and BayesHack for Bayesian statistics and probabilistic programming.

JavaScript

JavaScript -- sometimes shortened to JS -- is a high-level language rooted in scripting, making it easy to learn and understand. Though less efficient at raw data processing compared with low-level languages, JavaScript excels in middleware tasks, such as APIs and translating ML outputs into user dashboards and other formats. Although it's not a common choice, businesses with strong JavaScript development teams can still use JavaScript for ML and AI. JavaScript ML libraries include math.js for mathematics, TensorFlow.js for training ML models and Synaptic for neural networks.

Lisp

Lisp is one of the oldest high-level languages in common use today and was an early choice for AI programming. It focuses on symbolic data, logic and functional programming and is known for its flexibility and fast prototyping capabilities. Despite its age, Lisp remains influential in AI research, with tools and libraries like Clojure for ML tasks, Apache Commons Math and LISP-STAT for statistical computing.

Scala

Scala is a versatile, concise, high-level language sometimes regarded as a cross between Java's object-oriented syntax and Julia's emphasis on parallelism and distributed computing. It offers solid performance through its Java compatibility and use of JVMs, and it supports distributed computing with frameworks like Apache Spark. Scala is ideal for handling huge data sets and sophisticated ML algorithms. Libraries such as Apache Spark's MLlib and the Smile library enable native data classification, regression, clustering and filtering, while the Breeze library adds complex mathematical capabilities.

Future of ML and AI programming

In the years ahead, ML and AI programming are expected to benefit from the very technologies they helped advance. Regardless of language, low-code and no-code programming platforms are likely to make ML and AI programming tasks faster and more accessible to non-programmers. For example, instead of relying on a development team to create an AI-driven business analytics tool, a business team might use low-code or no-code platforms to build and train ML models and run analytics independently.

Similarly, AI-assisted code generation is already emerging with tools such as ChatGPT and Claude, which can write code in response to carefully formulated prompts. This capability lets users rapidly create algorithms or modules for AI, ML or any other development project. In effect, developers could use generative AI tools to improve and augment the code of ML models themselves, though human supervision and validation will remain vital.

Finally, fostering an environment of innovation and exploration is essential for staying ahead in this rapidly evolving field. A mature and diverse staff should be encouraged to seek continuing education in various languages and to explore new technologies in smaller, less critical modules. Wise and forward-thinking business leaders will recognize the value of supporting and facilitating such learning initiatives.

Stephen J. Bigelow, senior technology editor at TechTarget, has more than 20 years of technical writing experience in the PC and technology industry.