In this digital age, Artificial Intelligence and Machine Learning have become the driving forces behind numerous businesses. If you’re new to the field of machine learning, the toughest part of learning machine learning is deciding where to begin. 

Whether you are trying to refresh your machine learning skills or making a career transition into machine learning entirely, it is natural to wonder which is the best language for machine learning. With over 700 different programming languages in widespread use, and each having its pros and cons, discovering which is the best language for machine learning is definitely a tough task. The good news, however, is that as you begin your journey as a machine learning engineer, you’ll start to discover which programming language will be most suitable for a business problem you are trying to solve.

Regardless of the individual preferences for a particular programming language, we have profiled six best programming languages for machine learning:

Python

Python is a versatile yet simple programming language that is extremely popular among developers worldwide. Nearly 8.2 million software developers use Python for coding. It is one of the most sought-after languages in the fields of Machine Learning, Data Analytics, and Web development. The rising popularity of Python can be attributed to its flexibility, open-source nature, scalability, and powerful libraries. The adoption of Machine Learning worldwide has also contributed significantly to the rising popularity of Python.

R Programming

R is an open-source, visualization-driven programming language that is immensely popular in the Machine Learning environment. It is mostly preferred by professionals who do not have extensive knowledge of coding, such as data miners, statisticians, and analysts. R offers a good resource pool, which comes in very handy when developing ML apps.

Java

Java and Javascript are multipurpose programming languages, very popular for use in algorithms and ML apps. These languages are reliable, stable, object-oriented, and offer heavy data processing competencies. With strong frameworks, such as Rapid Miner and Weka, Java and JavaScript can support Machine Learning algorithms, decision trees, regression techniques, and more.

Julia

Julia is a high-level programming language, created especially for developing effective model analytics for developing ML apps. Thanks to its easy syntax, it is quite popular among developers. Different features such as a sleek compiler, numerical precision, distributed parallel execution, and a large mathematical function library make it one of the ideal programming languages for Machine Learning.

Lisp

Lisp is an old programming language but has now gained popularity for AI and ML-related projects. Known for its architecture and practices, Lisp offers developers limitless possibilities when it comes to developing ML apps. Salient features such as domain-specific language embedded with code, building owners, and more have made it quite popular.

C and C++

C and C++ are both powerful programming languages that are a developer's favorite across the world. These languages are considered low-level languages, making them easily readable by a machine. These languages offer easy hardware-level features that support ML apps that can be easily implemented on IoT devices.

Each language is good where it fits best

According to the industry experts, there is no best language for machine learning, each is good where it fits best. Yes, there is no single machine learning language as the best language for machine learning. However, there are definitely some programming languages that are more appropriate for machine learning tasks than others. Many machine learning engineers choose a machine learning language based on the kind of business problem they’re working on. For instance, most of the machine learning engineers prefer to use Python for NLP problems while also preferring to use R or Python for sentiment analysis tasks, and some are likely to use Java for other machine learning applications like security and threat detection. Software engineers with a background in Java development transitioning into machine learning sometimes continue to use Java as the programming language in machine learning job roles.