The most popular programming languages used in this Machine Learning Field are :

1. Python 👑

2. R

3. Julia

4. Java

5. Scala

6. Javascript

Python 🐍

Python is a high-level, interpreted programming language that is widely used in AI/ML development. Python's popularity in AI/ML development is largely due to its simplicity, readability, and vast ecosystem of libraries and frameworks that make it easy to develop and deploy AI/ML models. Some of the popular libraries and frameworks used in AI/ML development with Python include:

• TensorFlow: A powerful open-source software library for dataflow and differentiable programming across a range of tasks, including machine learning, deep learning, and neural networks.

• PyTorch: An open-source machine learning library based on the Torch library. It's primarily used for developing deep learning applications and provides a seamless path from research prototyping to production deployment.

• Keras: A high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It allows for fast experimentation and prototyping of neural networks.

• Scikit-learn: A machine learning library for Python that provides simple and efficient tools for data mining and data analysis.

R 🏁

R is an open-source programming language that is specifically designed for statistical computing and graphics. R's popularity in AI/ML development is due to its powerful statistical modeling capabilities and a vast range of packages and libraries. Some of the popular packages and libraries used in AI/ML development with R include:

• Caret: A comprehensive package for building and evaluating predictive models in R. It provides a unified interface for a wide range of machine learning algorithms.

• MLR: A machine learning framework in R that provides a unified interface to a large number of machine learning algorithms.

• TensorFlow for R: A package that provides an interface to the TensorFlow library in R. It allows R users to create and train deep learning models using TensorFlow.

Julia 🎒

Julia is a high-level, high-performance programming language designed specifically for scientific computing, numerical analysis, and machine learning. Julia's popularity in AI/ML development is due to its speed, simplicity, and ease of use. Some of the popular packages and libraries used in AI/ML development with Julia include:

• Flux: A powerful library for machine learning in Julia. It provides an intuitive, flexible interface for building and training deep learning models.

• MLJ: A machine learning framework in Julia that provides a unified interface to a large number of machine learning algorithms.

Java ☕️

Java is a general-purpose programming language that is widely used in AI/ML development. Java's popularity in AI/ML development is due to its strong support for object-oriented programming and a wide range of libraries and frameworks. Some of the popular libraries and frameworks used in AI/ML development with Java include:

• Deeplearning4j: A deep learning library for Java that is designed to be scalable and production-ready. It provides an interface to popular deep learning frameworks like TensorFlow and Keras.

• Weka: A collection of machine learning algorithms for data mining tasks. It provides a GUI for data preprocessing, classification, regression, clustering, and feature selection.

Scala 🧱

Scala is a general-purpose programming language that is widely used in big data and AI/ML development. Scala's popularity in AI/ML development is due to its functional programming capabilities and wide range of libraries and frameworks. Some of the popular libraries and frameworks used in AI/ML development with Scala include:

• Apache Spark: A fast and general-purpose cluster computing system for big data processing. It provides a rich set of APIs for machine learning and data analysis.

• Breeze: A numerical processing library for Scala. It provides a set of tools for linear algebra, numerical optimization, and machine learning.

Javascript ⚡️

JavaScript is a programming language that is widely used in web development, but it's also gaining popularity in AI/ML development. JavaScript's popularity in AI/ML development is due to its ubiquity on the web, which allows for the development and deployment of ML models directly in web browsers. Some of the popular libraries and frameworks used in AI/ML development with JavaScript include:

• TensorFlow.js: A library for machine learning in JavaScript. It allows for the development and deployment of ML models directly in web browsers or on Node.js.

• Brain.js: A neural network library for JavaScript. It provides a simple and easy-to-use interface for building and training neural networks.

• ConvNetJS: A library for deep learning in JavaScript. It provides a set of tools for building and training convolutional neural networks.

JavaScript's popularity in web development and its ability to run in web browsers have made it an attractive language for AI/ML development on the web. With libraries like TensorFlow.js, Brain.js, and ConvNetJS, JavaScript developers can build and deploy ML models directly in web browsers, making it easier to create interactive and engaging web applications that incorporate AI/ML functionality.

Overall, Python and R remain the most popular programming languages for AI/ML development, but other languages like Julia, Java, Scala, and JavaScript are gaining popularity as well. Personally, I have used only Python for all my personal projects due to reasons like availability of resources, and ease of learning.

---

Hope you liked this blog, till then you can follow me on @twitter & @github