The Top 10 Programming Languages For Machine Learning

Machine Learning (ML) is now in-demand. The broad range of coding languages used in computer technology can easily make it difficult to understand the best language to learn ML within the field. Learning programming languages offer significant breakthroughs to break into machine learning yet often, people wanting to learn programming languages don’t know where to begin.

The machine learning industry is so vast that there are a variety of programming languages that are used to develop creations and perform different actions. It depends on what is being built and what the objective is.

So, what are the best programming languages to learn? Here are the Top 10 popular languages in-demand today.

1. Python – Python is typically used to focus on core functionality of an application. It

is the most popular and in-demand language in business. Its growth and demand have risen far beyond other languages such as JavaScript, which could be used to create or perform almost anything. Its speed and algorithm performance are essential for ML and is the go-to language within AI.

2. R – R is very useful for a statistical purpose. It is effective for the analysis and

manipulation of statistics to help develop mathematical algorithm formulae. R has a variety of packages used in ML to help implement machine algorithms to promote performance.

3. Lisp – Lisp was founded by John McCarthy in 1958, the father of AI. It processes

symbolic information efficiently and creates dynamics of new systems or objects. This is something now taken over by other evolved languages but is will always perform AI algorithms efficiently.

4. Prolog – Prolog is another language used when AI was originated. Its strengths are pattern matching and automatic backtracking which helps to keep programming frameworks flexible. A very efficient programming language that helps design expert Machine Learning. 5. Java – Java is considered a significantly challenging programming language to learn but offers huge benefits when you get to grips with it. Java is excellent for big data, a pre-requisite in machine learning, and is great for search algorithms and genetic programming. 6. JavaScript – JavaScript is typically considered for web development and interaction

and is a great choice for web and front-end developers. However, JavaScript is still considered amateur for ML but is gaining speed to capture a range of functionality used within the likes of R and Python.

7. C++ – C++ emerged from the 1980s and is one of the oldest programming

languages. With a high-level programming ability. It’s well known to manage intensive ML applications where execution of performance requires high speed and resources.

8. C# – Known as C-Sharp, is an open-sourced, versatile programming language. Its

cross-platform capabilities are particularly good for developers that use ML.NET software for machine learning. It helps strategize different machine learning processes.

9. Shell – Shell has drawn comparisons to Python in the past yet is not that powerful. It

focuses on basic algorithms but is built with various ML tools to provide an open

environment that is focused on deep learning.

10. Julia – Julia functions with the combination of programming languages such as

Python and R at the speed of C++ and Java. Julia aims to embed basic machine

learning algorithms to produce state-of-the-art ML frameworks.

Sophie Turton