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Top 10 AI Programming Languages: A Beginner's Guide to Getting Started

Explore the world of AI programming languages in this beginner-friendly guide. Learn about the top languages, their uses in AI, main packages, the roles that rely on them and resources to get you started.
Jun 2024  · 20 min read

As artificial intelligence (AI) continues to transform industries, many beginners are eager to get involved. Choosing the right programming language is an important first step.

In this blog post, I’ll introduce you to the top 10 AI programming languages. I’ll also cover how they are used in AI, highlight their top packages and libraries, discuss the AI roles that primarily use them and provide resources to help you get started.

What Are AI Programming Languages?

AI programming languages are tools used to build applications that simulate human intelligence. These languages make the creation of algorithms capable of learning from data and making decisions possible. Each language supports its own set of libraries and frameworks, enhancing the development of tasks such as speech recognition, data interpretation, and autonomous driving.

Choosing a language that simplifies coding and offers libraries tailored for tasks like machine learning and data analysis is essential. For example, Python is renowned for libraries like TensorFlow and PyTorch, while R is favored for statistical analysis.

AI programming languages form the backbone of intelligent systems, marrying code with the capability to handle complex AI-specific tasks efficiently.

Top 10 AI Programming Languages

Here’s my curated list of the top 10 AI programming languages in 2024. Leading the list by a significant margin is Python.

1. Python

Python is the most popular and user-friendly programming language in the field of AI. Its clear syntax and wide range of libraries and packages make it valuable.

Leading platforms such as TensorFlow and PyTorch help us build powerful AI models, while libraries such as scikit-learn provide tools for typical machine learning assignments. This blend of convenience and advanced features makes Python the expert’s choice for developing AI applications.

  • How is Python used for AI? Python is widely used in AI for its comprehensive range of applications, spanning the entire spectrum of artificial intelligence.
  • Top Python AI packages and libraries:
    • NumPy: It can handle complex mathematical structures effortlessly.
    • pandas: It provides user data structures such as DataFrames for analyzing and manipulating data.
    • Matplotlib: It is a visualization tool widely used for creating static, animated, and interactive graphs and charts.
    • scikit-learn: It offers a range of tools and algorithms for machine learning. 
    • TensorFlow: Google's brainchild provides a robust foundation for constructing and implementing neural networks.

AI roles that use Python

Python is fundamental for AI professionals, providing essential tools for roles ranging from machine learning engineers to computer vision experts. Learn more about advancing your skills through our courses on becoming a data scientist, becoming a data engineer, and the machine learning engineer track. Here are some AI job roles that use Python:

  • Machine learning engineers: They are responsible for creating and implementing machine learning models using Python libraries such as TensorFlow, PyTorch, and scikit-learn. 
  • Data scientists: They utilize Python for tasks like data analysis, cleaning, manipulation and visualization using libraries like Pandas and Matplotlib. 
  • Natural language processing engineers: They build and deploy systems that can understand and process human language to handle tasks such as machine translation.
  • Computer vision engineers: They enable machines to analyze visuals effectively. Libraries like TensorFlow provide functionalities for tasks such as video processing.
  • Research scientists: They use Python to prototype and implement new AI algorithms. The Python library and packages cater to a variety of science-related fields.

Top Python resources to get started

Python is popular for its ease of use among newcomers. Choose from our courses, tutorials, or blog posts to help you get started:

  1. How to learn Python
  2. Introduction to Python
  3. Intermediate Python
  4. Data manipulation with pandas
  5. Machine learning fundamentals with Python

2. R

R is a statistical program that focuses on data analysis (including cleaning, visualization, and interpretation), machine learning and statistical modeling. 

  • How is R used for AI? R has strong statistical capabilities, and its collection of data visualization features has made it a popular option for creating models and developing prototypes for machine learning algorithms.
  • Top R AI packages and libraries:
    • Tidyverse: It is not a single library. It is a set of packages that collaborate smoothly on data science tasks. 
    • caret: It is a one-stop shop for machine learning tasks in R. It provides a unified interface for various machine learning algorithms, allowing users to easily train, tune, and evaluate different models.
    • XGBoost: It is a powerhouse for gradient boosting, a popular machine-learning technique known for its accuracy and efficiency.
    • randomForest: As the name suggests, randomForest implements the random forest algorithm, a popular ensemble learning technique that combines the predictions of multiple decision trees.
    • IML (Interpretable Machine Learning): It provides a framework for building statistical models in R.

AI roles that use R

R is integral to specific niches within AI, particularly where statistical expertise and detailed data analysis are paramount. This versatile language supports a range of AI roles, from data science to bioinformatics, each leveraging its powerful statistical and visualization tools. Here are some AI job roles that use R:

  • Data scientist (specializing in R): Although Python is widely used in data science, R remains a player in tasks that involve statistical analysis and modeling. 
  • AI researcher (focused on statistical modeling): Researchers often prefer using R because of its computing and visualization capabilities. They can apply R to develop, test, and evaluate models that contribute to the progress of AI algorithms.
  • Bioinformatics scientist: They use R for tasks like gene expression analysis and variant discovery, which are essential for AI-driven applications in genomics and bioinformatics.
  • Econometrician (applying AI in economics): They develop AI models for predicting trends, assessing risks, and forecasting market behavior.
  • Social data scientist: They use R for social network and text analysis tasks.

Top R resources to get started

R provides strong statistical analysis, clear data visualizations, and a user-friendly environment for building machine-learning models. Explore our selection of courses, tutorials, and blog entries. Here are some options to guide you as you begin:

  1. Introduction to R
  2. Intermediate R
  3. Introduction to Tidyverse
  4. Exploratory Data Analysis in R
  5. Machine learning scientist with R

3. Julia

Julia boasts quick processing speed and the capability to handle complex calculations, making it perfect for industries that require extensive data analysis.

  • How is Julia used for AI? Julia’s speed and efficiency make it the perfect choice for advanced machine learning models.
  • Top Julia AI packages and libraries:
    • Flux.jl: It is a popular learning framework that provides an intuitive platform for building and fine-tuning neural networks.
    • Mocha.jl: It is the perfect tool for data visualization tasks. 
    • DiffEq.jl: It provides a robust and efficient framework for solving differential equations of various types.
    • StatsBase.jl: It is Julia's standard statistics library.
    • Makie.jl: It aids in effective data visualization by comprehending intricate data and model behaviors.

AI roles that use Julia

Julia is increasingly recognized for its high-performance capabilities in AI. This dynamic language is being used across different AI specializations, from robotics to financial analysis, enhancing both the development and deployment of innovative solutions. Here are some AI job roles that use Julia:

  • AI engineer: They utilize Julia’s capabilities to construct and train state-of-the-art models that are robust.
  • AI researcher (algorithmic optimization): They build prototypes and evaluate and assess the effectiveness of their algorithms before transitioning them to commonly used languages for operational use.
  • Machine learning engineer: They take advantage of Julia’s libraries, such as Flux.jl, to create AI solutions for industries such as finance.
  • Robotics engineer (AI-driven robotics): Julia’s capacity to handle real-time data and integrate it with robotics frameworks like ROS (Robot Operating System) positions it as a tool for robotics engineers working on AI-driven robots.
  • Financial analyst (AI in trading): Julia's strengths in numerical computing and optimization present it as an asset for analysts crafting AI-based trading algorithms and risk management models.

Top Julia resources to get started

With its expanding collection of deep learning tools and a solid basis in math, Julia is great for crafting AI models. You can select one of our courses to get started:

  1. Introduction to Julia
  2. Intermediate Julia
  3. Data Manipulation in Julia
  4. Introduction to data visualization in Julia
  5. Introduction to machine learning with Julia

4. Scala

Scala excels in machine learning and AI due to its scalability and integration with Apache Spark. Its functional programming capabilities and compatibility with Java contribute to its effectiveness in developing AI systems.

  • How is Scala used for AI? Scala excels at handling data and functional programming and manages large-scale AI applications, mainly when working alongside Apache Spark. 
  • Top Scala AI packages and libraries:
    • Apache Spark: It provides features such as distributed computing, data streaming, and machine learning, making it a fundamental tool for developing AI applications in Scala.
    • Spark MLlib: It offers a range of machine learning algorithms for tasks such as classification, regression, clustering, and building recommendation systems.
    • Breeze: It includes operations in linear algebra, statistical functions and optimization methods, offering a basis for constructing AI models using Scala.
    • Deeplearning4j: Although DL4J is primarily Java-based, it provides a Scala interface for constructing and educating learning models. It accommodates network structures and seamlessly integrates with Apache Spark.
    • Scopt: It is not categorized as an AI library, but it enables developers to design user command line interfaces (CLIs) to manage and set up their AI models, simplifying their usage and deployment processes.

AI roles that use Scala

Scala is used in environments where large-scale data processing and complex algorithmic challenges prevail. This language supports a variety of AI roles and offers unique advantages in systems engineering and big data integration. Here are some example job roles that use Scala:

  • Machine learning engineer: They build and deploy machine learning models and large-scale systems.
  • Data scientist: They use it for data cleaning, manipulation, and building custom machine learning pipelines.
  • Spark developer: Apache Spark is a big data processing framework, and Scala is one of its primary languages. If a company uses Spark for AI tasks, there will likely be Spark developers working on that.
  • AI researcher: Researchers at the forefront of AI use Scala for prototyping new algorithms or building specialized tools due to its flexibility and performance.
  • Scala backend developer (for AI): They use Scala to build and maintain the server-side functionality.

Top Scala resources to get started

Scala offers a foundation for managing datasets and creating sophisticated AI solutions. With its focus on programming and seamless connection to Apache Spark, Scala is an asset for working with big data and developing innovative AI projects. 

Here’s a resource to help you get started with Scala:

  1. Introduction to Scala

5. Java

Java is commonly used in AI to develop sophisticated software applications. It is known for its ability to build adaptable systems compatible with various computer platforms and integrate with AI libraries to implement machine learning algorithms.

  • How is Java used for AI? Java is used in AI to build large-scale, cross-platform applications that integrate AI functionalities.
  • Top Java AI packages and libraries:
    • WEKA: It is a collection of machine-learning algorithms.
    • DeepJAVA: It provides tools for building and training deep neural networks in Java.
    • H2O: It makes use of distributed computing to train models effectively.
    • Deeplearning4j (DL4J): It is a deep learning library for models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
    • Smile: It specializes in efficiency and clear syntax for developing machine learning models.

AI roles that use Java

Java remains a cornerstone in AI development, offering robust frameworks and libraries that help with the integration of AI into various platforms, from big data systems to mobile applications. Here are some of the AI job roles that use Java:

  • Big data engineer: They use Java frameworks, which provide tools for distributed processing and machine-learning algorithms, to construct and manage AI pipelines that handle large volumes of data.
  • Enterprise AI developer: Java libraries such as Deeplearning4j (DL4J) or H2O to create and integrate AI models into current enterprise Java systems. This enables businesses to tap into AI features without investing in more complex infrastructure.
  • Android AI developer: They use tools like TensorFlow Lite—an optimized version of TensorFlow for mobile—to incorporate AI-driven functionalities into Android applications.
  • AI infrastructure engineer (Java backend for AI): They develop APIs in Java to expose AI models or features to applications. They might also create pipelines and monitoring systems using Java to ensure that AI models operate smoothly in production environments.
  • Software engineer (AI integration with Java systems): They integrate AI models or libraries like scikit-learn or spaCy into these Java-based applications through Java APIs, allowing them to use AI capabilities.

6. C++

C++, known for its powerful processing capabilities, excels in handling AI assignments such as computer vision and robotics thanks to its precise hardware control and strong performance. 

  • How is C++ used for AI? C++ is commonly used in AI to handle tasks that require a level of computational power and precision in managing hardware resources.
  • Top C++ AI packages and libraries:
    • Eigen: It is an advanced linear algebra toolkit. 
    • Armadillo: It's another algebra library that provides syntax similar to MATLAB.
    • Dlib: It delivers real-time results, making it ideal for creating AI applications embedded in devices.
    • OpenCV: It offers a range of algorithms and tools for handling images, detecting objects and more.
    • TensorFlow C++ API: The C++ interface manages TensorFlow functions and enables developers to utilize C++'s speed for the demanding components of their AI models.

AI roles that use C++

C++ enables developers to leverage its speed and close-to-hardware capabilities to design and implement sophisticated AI applications that require real-time operation and optimal resource management. Here are some job roles that use C++:

  • High-performance computing (HPC) AI engineer: They use C++ to build and train complex models that allow fine-grained control over hardware resources.
  • Embedded systems engineer (AI on edge devices): They use  C++ to develop AI models that run efficiently on resource-constrained devices like mobile phones. This is crucial for applications like on-device object detection or real-time decision-making.
  • Robotics engineer (real-time AI for robots): They use C++ to develop core functionalities for robot control, integrate AI models for tasks like path planning or obstacle avoidance, and ensure real-time performance for robots operating in dynamic environments.
  • Game AI programmer: They use C++ to implement complex AI behaviors for non-player characters (NPCs) in games.
  • Computer vision engineer (performance-critical vision tasks): They use C++ libraries like OpenCV for tasks like image processing, object detection, or video analysis, especially when performance and low-latency execution are critical requirements.

7. Javascript

JavaScript is used to develop applications such as chatbots and recommendations and for rapid prototyping. 

  • How is Javascript used for AI? JavaScript builds interactive AI features in web browsers, like chatbots, and prototypes AI ideas quickly with libraries like TensorFlow.js.
  • Top Javascript AI packages and libraries:
    • TensorFlow.js: You can use it to develop and train machine learning models, with JavaScript allowing you to incorporate AI-driven functionalities into web applications.
    • Brain.js: It provides an interface similar to TensorFlow.js, making it more accessible for newcomers.
    • Synaptic.js: It provides a flexible API for building and training various neural network architectures in JavaScript.
    • Keras.js: It enables you to utilize Keras’ advanced features in a JavaScript setting. 
    • Ml5.js: It provides a range of ready-made machine learning for JavaScript applications.

AI roles that use Javascript

Javascript is used by developers aiming to integrate AI into web interfaces, from AI-driven visualizations to real-time user interaction. Here are some examples of roles that use Javascript in AI:

  • Front-end web developer (AI-powered user interfaces): They use JavaScript libraries to build interactive and intelligent user interfaces. These could involve features like face recognition in video chat or voice recognition for search functionalities.
  • Web AI developer (building standalone AI web apps): They build full-fledged AI applications directly in the browser using JavaScript frameworks like Synaptic.js.
  • AI visualization developer: They visualize complex data generated by AI models, making it easier for users to understand the model's outputs and insights.
  • Conversational AI developer (AI-powered chatbots): They use libraries that provide JavaScript SDKs to build chatbots that can interact with users in a more natural and engaging way.
  • Full-stack AI developer (JavaScript with backend AI): They leverage Javascript's faster iteration cycles when building AI-powered web applications.

8. Lisp

Due to its adaptability, Lisp is used in reasoning tasks, such as language processing.

  • How is Lisp used for AI? It is used for symbolic reasoning tasks because it can manipulate symbols and data structures.
  • Top Lisp AI packages and libraries:
    • Common Lisp User Environment(CL-USER): This doesn't refer to a library but rather to an environment that serves as a platform for engaging with Common Lisp. It offers features for handling data, managing program flow, and interacting with symbols, all essential for AI-related tasks.
    • Applicative Common Lisp(ACL2): Computational logic designed to support automated reasoning and theorem proving. It enables the representation of theories and facilitates deductions.
    • McAll: It provides features, for machine learning algorithms with an emphasis, on machine learning methods.
    • OpenCyc: This project is an initiative in the field of open-source knowledge bases developed using Lisp. Its goal is to assemble a repository of knowledge and logical reasoning abilities that can benefit areas such as understanding natural language and answering questions in artificial intelligence.
    • Lisp implementations with AI functionalities: Numerous versions of Lisp, such as Apache CLIME and GNU CLisp, come equipped with functions or libraries designed for handling tasks related to AI.

AI roles that use Lisp

Lisp is especially valued in research, education, and the maintenance of legacy AI systems, providing a rich environment for exploring and teaching AI concepts. Here are some AI roles that use Lisp:

  • AI research scientist (Lisp-based research): Some researchers use it for rapid prototyping in academic or research settings.
  • AI language engineer (Lisp compiler development): They develop and improve Lisp compilers, interpreters, or libraries that enhance their capabilities for AI applications.
  • Legacy AI system maintainer: Some companies might have existing AI systems built with Lisp from a previous era. Developers with Lisp expertise would be crucial for maintaining, debugging, and potentially migrating these legacy systems.
  • Open-source AI developer (Lisp-based projects): Some open-source projects might utilize Lisp for specific functionalities. Developers with Lisp skills could contribute to these projects, furthering AI research and development advancements.
  • AI educator (teaching Lisp for AI concepts): While not directly an AI development role, Lisp can be a valuable tool for teaching core AI concepts. Educators with Lisp expertise could introduce students to symbolic reasoning, functional programming paradigms, and problem-solving approaches in AI using Lisp as a platform.

9. Haskell

Due to its functional programming and strong typing, Haskell offers clean and reliable code. This makes it ideal for AI research, prototyping new algorithms, and teaching core programming concepts.

  • How is Haskell used for AI? Haskell is used in AI for research, prototyping algorithms, and education because it focuses on clean, reliable code from functional programming and strong typing.
  • Top Haskell AI packages and libraries:
    • HLearn: It offers a variety of machine-learning algorithms that are coded in Haskell.
    • Bayesbayes: It specializes in programming, a method for handling uncertainty in artificial intelligence.
    • Halcyon: It deals with reasoning, which involves making conclusions based on information.
    • BioCondor: It utilizes Haskell for creating workflows and overseeing tasks related to distributed computing not for AI purposes.

AI roles that use Haskell

Haskell is recognized for its mathematical rigor and functional programming features, making it a preferred tool for AI researchers and developers in fields requiring high reliability and precision. Here are some AI roles that use Haskell:

  • AI researcher: They utilize Haskell's immutability and strong typing to formally verify AI algorithms, which is crucial in safety-critical systems like healthcare or autonomous vehicles.
  • Probabilistic programming specialist: Leveraging Haskell’s type system and libraries such as Monad transformers, they build models that handle the complexities and uncertainties of real-world data, essential for Bayesian inference and probabilistic reasoning.
  • Functional verification engineer: They use Haskell’s functional nature and type system to help create precise specifications for AI systems, facilitating rigorous testing and verification processes.
  • Domain-specific language developer: They use Haskell's functional style to develop clear and concise DSLs, making advanced tools accessible to domain experts without extensive programming knowledge.
  • Research scientist: They work on developing deep learning frameworks based on functional programming principles, aiming to enhance the efficiency and expressiveness of model design and training.

10. Mojo

Mojo makes AI development easier with user code that maintains speed. It delivers performance and adaptability, but it's still catching up regarding library support compared to older languages.

  • How is it used for AI: Mojo offers a user-friendly language with high performance for building efficient and scalable AI applications.

Note: At the time of writing this blog post, Mojo is a promising new language. However, it's still under development and hasn't yet achieved widespread adoption in the industry. Currently, no established roles specifically require expertise in Mojo.

How to Learn AI Programming Languages

Starting your journey in AI requires mastering key skills such as basic mathematics (like linear algebra and calculus) and statistics, which are foundational for understanding AI operations and algorithms. 

Programming knowledge, particularly in Python, is essential due to its simplicity and the extensive resources available for AI. Skills in data manipulation and understanding of data structures are integral to implementing AI solutions. 

Continuously updating these skills through practical projects and targeted learning tracks can effectively build your expertise in AI.

For a comprehensive guide on learning AI programming from scratch, including detailed learning plans and resources, check out how to learn AI from scratch.

How to Get Started with AI

Here are some additional resources to help you get started on your AI journey:

  1. What is AI? A Quick-Start Guide For Beginners: This guide provides an overview of AI, including its applications in various sectors. It breaks down the AI and machine learning workflow into easy-to-understand steps and includes a comprehensive guide on how to start learning AI from scratch, making it perfect for beginners.
  2. Implementing AI Solutions in Business: Getting Started with AI: This course covers the basics of AI, its evolution, and how it differs from other data solutions. It includes a framework for implementing AI in business, key elements of an AI strategy, and phases for implementation. It also emphasizes the importance of responsible AI governance.
  3. Understanding Artificial Intelligence Course: This beginner-friendly course explores the foundational aspects of AI, including machine learning, deep learning, and natural language processing. It features hands-on exercises to help you understand the practical applications and societal impacts of AI.
  4. Introduction to AI Agents: Getting Started With Auto-GPT, AgentGPT, and BabyAGI: This resource explores AI agents and discusses using tools like Auto-GPT, AgentGPT, and BabyAGI. It covers these AI agents' potential dangers and limitations and provides guidance on accessing and using them effectively.
  5. AI Fundamentals Certification: This certification covers the essential concepts of AI, including machine learning and generative AI. It's ideal for those who need a comprehensive understanding of AI principles and their applications.

Starting with AI programming is exciting and full of potential. Choose your language, start experimenting, and learn as you go!


What makes a programming language good for AI?

A good AI programming language is supported by extensive libraries and frameworks that simplify the implementation of AI algorithms. It should also have a strong community and ongoing development to keep up with AI advancements.

Can I use more than one programming language for AI?

Yes, many AI professionals use multiple programming languages to leverage the unique strengths of each, such as Python for machine learning and JavaScript for AI-driven web applications.

How long does it take to learn an AI programming language?

The time it takes can vary based on your background and the language complexity. Generally, with consistent study, beginners can grasp the basics in a few months and continue developing more advanced skills.

What is the best AI programming language for beginners?

Python is often recommended for beginners due to its readability, wide range of learning resources, and strong support for AI development in libraries like TensorFlow and PyTorch.

Are there any new or up-and-coming AI programming languages?

Established languages like Python and R continue to dominate, but newer ones like Julia and Mojo are gaining attention. Mojo, in particular, is noted for its specific focus on AI-driven applications, offering optimized features for machine learning tasks.

Photo of Vinita Silaparasetty
Vinita Silaparasetty

Vinita Silaparasetty is the Chief Data Scientist of Trendwise Analytics, an author, and speaker. She holds an MSc. in Data Science from Newcastle University in the U.K. She specializes in Python, R and Julia for Generative AI, Machine Learning and Deep Learning. Her expertise includes using Tensorflow and Keras for neural network model building.


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