Programming languages Used In Machine learning

Programming languages Used In Machine learning

Machine learning is writing code/work-flow for machines to make decisions based on pre-defined algorithms and experiences Everyone entering the field of Machine Learning whether its a professional or a freelancer chooses its programming language in Machine Learning to implement in their projects. It’s hard to choose or mention a programming language to work on because its the type of project to be worked on, comfort level, and adaptability of the user.

Here are some of the most popularly used programming languages in Machine Learning that will be useful for developers and programmers for writing algorithms in Machine Learning:


Programming languages Used In Machine learning

Developed by James Gosling at Sun microsystem, Java has been in existence for a long time and works well in machine learning. Some of the advantages of using Java include a large base of programmers, ease of use, maintenance and debugging, and readability. Java is usually focused on network security and fraud detection algorithms.

The most popular Machine Learning toolkit for Java is Weka which contains tools for data pre-processing, classification, regression, clustering, and visualization. It is regarded as a secure language due to its use of bytecode and sandboxes. It is a programming language that will reward future machine learning systems with speed, accuracy, and precision. 


Python is the primary programming language used in Machine Learning that is used for most of the research and development in Machine Learning, it’s easy to understand, widely adopted, and offers rapid prototyping compared to other programming languages. It is the primary choice for most of its users especially for beginners due to the release of TensorFlow, dynamic programming language, Object-oriented (ooPs) concepts, portable and extensible nature, supportive community, and a wide selection of libraries. TensorFlow and Pytorch are some of the libraries that are useful in deep learning.

Another reason for its popularity is that its syntaxes are very simple and can be easily learned which makes algorithms easily understandable. Simplicity in Python syntax means that developers can focus on actually solving the Machine Learning problem rather than spend all their precious time understanding just the technical aspects of the language, thereby giving direct access to users for predictive analysis.


A graphics-based programming language used for statistical computing, analysis, and visualizations key aspects in Machine Learning. R is a full-fledged software with Object-Oriented language, support functional & procedural language. Easily programmable code also supports Machine Learning development libraries & tools. R is free and open-source which means users can access it easily and learn from the ASCII text file and can modify it as per their needs.

R is extremely employed in bioengineering, bioinformatics, and biomedical statistics. It gives users access to a substantial number of leading-edge packages, a number of them are RODBC, G-models, Class, and TM enabling easy implementation of Machine Learning algorithms.


Scala supported by Apache Spark platform, high-level language that combines functional and object-oriented programming with high-performance runtime. It’s one of the core languages used in Machine Learning. Good for vigorous programming with many developers, programmers, and teams. Scala is generally more into Data Management for creating and working with sensitive algorithms. It can leverage many of the same functions as Python, such as building machine learning models.

Implementation of Scala in Machine Learning algorithms even resulted in securing Application. There are many well-developed libraries of Scala are suitable for linear algebra, number generation, and scientific computations. Saddle, a library supported by Scala, provides a strong foundation for data manipulation through array-backed support, robustness to missing values, automated data alignment, and 2-Dimensional data structures.


Julia being centered on high-performance numerical analysis is a dynamic language best armed to address Deep Learning. Designed for formulating basic mathematics and scientific queries in a quick manner. Its syntax is as easy as python is and is similar to that of Microsoft’s Basic, which ensures that Julia is easier in applications involving transition. It’s not an Object-oriented programming language.

Julia developers have made pre-processing evolved than anyone ever would have originally thought possible. As a result, Julia has become a much more viable and likable option for machine-learning. Julia provides execution speeds similar to C and C++, with an add on parallelism aspect in its model. It is scalable and much faster than python and R. Counting together all pros and cons Julia can be thought more of as a bridge between python and spark.

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