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Best Languages for Machine Learning


The global machine learning market is expected to reach the value of $20.83B in 2024 from $1.58B in 2017 – growing at a CAGR of 44.06%.

The faster adoption of machine learning across industries reflects the effectiveness of its frameworks, algorithms, and techniques at solving real-world complex problems. Machine Learning is a powerful branch of Artificial Intelligence that embeds science behind machines to do human-like activities. Without any manual effort, ML-based models have the capability of taking real-time decisions.

Machine learning is gaining ground with its state-of-the-art solutions. The world is looking forward to seeing its latest offerings, add-ons, and contributions. Machine learning programming languages are revolutionizing making models even more reliable and sustainable in a real-world environment.

Reading Recommendation

“Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems” by Aurelien Geron is one of the best books to start with machine learning.

The book discusses:

  • Tools and concepts of building intelligent systems.
  • Techniques of implementing linear regression and deep neural networks.
  • How programmers can use machine learning with the help of efficient tools to implement ML-based programs from huge datasets.
  • A practical approach towards developing machine learning models using Scikit-Learn, Keras, and TensorFlow.
  • Less theory and more examples to use production-ready programming frameworks.

And much more… This book is available on Amazon. Below are some best programming languages for machine learning to develop strong neural networks and projects.

1. Python

Python is considered one of the best languages of machine learning. It is a simple yet versatile programming language that can create complex web apps and frameworks. Due to its support for multiple libraries, python is a primary language to use in machine learning and data analytics systems. Python fully supports:

  • Scalability
  • Flexibility
  • Visualization
  • Object-oriented programming
  • Scientific computing
  • Imperative and functional development
  • Sentiment analysis
  • Natural language processing
  • Core machine learning libraries such as seaborn, sklearn, Tensorflow, Scikit, etc.


It is a popular programming language and is preferred by developers all across the globe. C/C++ helps implement machine learning models with its strong support of libraries such as Tensorflow, Torch, etc. C/C++ develops complex systems and has the capability of manipulating algorithms to achieve expected results at a detailed level. Moreover, when it comes to controlling performance, C/C++ stays abreast of many latest programming languages.

R Programming

R is a scalable andbest programming language for deep learning. It is popular for developing huge machine learning environments. Due to its support for command line and scripting, professionals with less grip over coding can learn and use it for developing ML-based applications. R is well-known for implementing regression techniques, classification, and decision trees.


Machine learning using the Shell is executed by the Unix shell. With its simple syntax and scripting nature, Shell is considered one of the best choices for developing machine learning models. Shell supports all operating systems. Its scripts and commands are quite helpful in data collection, preparation, and processing.


Java and JavaScript are proven programming languages in developing machine learning algorithms. Both languages help create enterprise-level scalable and reliable systems. Java and JavaScript have the capability of implementing machine learning models with high precision, speed, and accuracy.


Go (Golang) is also a suitable programming language for machine learning. Go is easy and simple to learn. It is light in execution, therefore, supports the development of huge neural networks with large datasets. It is also a popular language to use in cloud computing applications due to its resemblance with features of the C language.


Julia is a special and dedicated programming language to develop machine learning applications. It has an easy syntax and offers distributed parallel execution with numerical precision and scientific computing.


Scala is known as a compiled language that has the power to compile executable code faster than any other programming language. Machine learning models are mostly developed on huge neural networks and large datasets that take enough time to execute. Scala helps deal with enterprise-level machine learning projects with its compatibility with Java libraries and frameworks. Therefore, it is used to develop projects related to scientific computing, data manipulation, automation, and big data.


Lisp is an old and best programming language for machine learning and AI. Developers use it to develop project architectures and AI-based applications. It is a domain-specific language, helps create dynamic objects, and is considered good for prototyping. It runs operations very smoothly.

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