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Best Languages for Machine Learning in 2022

Vishal Srivastava
Freelance content marketer for recruitment niches - - - 3 min. to read

In a fast-changing world and dynamic systems, machine learning has become an essential part of a technology stack to predict possible outcomes in complex systems at scale. If you work with a lot of data, sooner or later, you’d need to pick the best language for machine learning.

If you’re looking to hire developers in machine learning, you must keep in mind that expertise and experience in programming are fundamental to applying machine learning models successfully. Knowledge of core programming languages and their prominent aspects such as algorithms, data structures, logic, and memory management is essential for the best machine learning results.

However, choosing the most suitable language for machine learning is also the key. In this article, we look at some of the best machine learning programming languages available in 2022.

But First, What is Machine Learning?

Machine learning falls under the domain of artificial intelligence. It enables software programs to improve their ability to anticipate outcomes without being programmed to do so. To forecast new output values, machine learning algorithms utilize past data as input.

Machine learning has libraries that make it easier for developers to incorporate machine learning logic into their applications using particular programming languages.

A number of programming languages can be used to build machine learning models. The following is a list of the top ten programming languages for machine learning.

What’s the Best Language for Machine Learning in 2022?

#1 Python

Python can power complicated scripting and online applications, making it a perfect choice for beginners and expert developers alike. It is a general-purpose programming language that was developed in 1991. 

Because of its simplicity and ease of use, it has long been a favorite among developers. It is used with a variety of different frameworks and libraries.

It’s no surprise that Python engineers are in high demand since it’s one of the most sought-after languages in machine learning, data analytics, and web development. Python is a popular programming language because it provides a wide range of options for coding. 

Multiple visualization packages and important core libraries like sklearn and seaborn are available because of their open-source nature and scalability. These powerful libraries make coding a breeze and allow machines to learn more.

Programming models supported by Python include object-oriented, functional, imperative, and procedural. Both TensorFlow and Scikit are pretty popular among Python developers when it comes to machine learning frameworks. 

Python is suitable for:

  • Prototyping
  • Scientific computation
  • Sentiment analysis
  • Natural language processing
  • Data science.

#2 R Language

For statistical computation and machine learning, R is a prominent open-source data visualization-driven language. It also has a large number of resources for building machine learning applications, 

Analysts, statisticians, and data miners use R because it is easier to learn than other programming languages. It supports a command-line interface and various IDEs in addition to a wide range of tools for managing libraries and producing better graphs.

It is a graphics-based language that data scientists use to analyze graphs by large corporations, particularly in the biomedical industry.

Representational State Machines (RSMs) such as classification, regression, and decision tree building may be implemented in R. It has been a dynamic, imperative, and functional language because of its statistical and functional properties. In addition to Windows, Linux, and OS X, it can also run on other platforms. 

R is currently the 15th most popular programming language as per the Tiobe Index. With such interest in the developer community, you can find skilled R developers easily for your project. 

#3 JavaScript – Java

Several machine learning applications and techniques can benefit from JavaScript and Java. These languages are known for their stability and dependability, and they are object-oriented and can handle a lot of data.

For machine learning methods, such as decision trees and regression, there are powerful frameworks like Weka and Rapid Miner in Java. 

Moreover, Java is particularly an excellent fit for enterprise-level software. On the other hand, JavaScript is used in almost all front-end development projects by large corporations. 

These technologies rely on the many machine learning libraries linked with them to succeed in machine learning applications. 

Fraud detection, cyber-attack detection, and network security are some of the applications where Javascript experts use these languages in machine learning.

#4 Julia

Julia is a popular high-level, dynamic programming language for constructing machine learning applications that are focused on model analytics. It has a simple syntax that makes it a popular choice for programmers since it also offers high performance.

It offers: 

  • Exceptional numerical accuracy
  • An efficient compiler
  • Distributed parallel execution
  • An extensive library of mathematical functions.

Interactivity is considered a beneficial feature when writing scripts for this language since it runs well on several platforms. It has a huge following because it is functional and object-oriented, making it an excellent option for constructing machine learning applications. 

It is free and open-source by design under the MIT license. Julia can function at its best on both the server and the client-side. 

It’s an excellent tool for calculating numerical and computational statistics so this is also quite popular amongst data analysts and statisticians.

#5 Lisp

When it comes to artificial intelligence and machine learning, Lisp is an ancient programming language that has recently gained popularity. Some developers prefer to use it because of its design and techniques, particularly AI and machine learning. 

Lisp was invented by John McCarthy, the father of artificial intelligence, and hence has its unique benefits. One of the biggest advantages of Lisp in machine learning is that it enables speed in prototyping and experimentation. 

In AI applications, it is critical to adopt an agile approach because development often happens in phases. Lisp is ideal for such iterative development cycles. It provides its creators with an almost infinite number of options such as list processing, symbolic expression (usage of symbols for computing that is quite useful in machine learning applications). 

Programming can operate more smoothly thanks to an automatic waste collection mechanism. Domain-specific language incorporated in code, building owners, and other features have made it famous. 

Despite its increasing popularity since 2000, Lisp still lags behind industry giants like Java, Python, and Javascript. In the 1970s, when C became well known, and Java was actively promoted, it fell out of favor.

As a result, the language is not widely used, making it difficult to locate programmers passionate about it. Moreover, using Lisp would also require some reconfiguration to ensure compatibility with new hardware and software thereby making it less popular than other languages for machine learning.

#6 Scala

While there are several programming languages in the world, Scala is gaining a lot of attention because of machine learning. With Scala, the executable code runs quickly, thanks to its use of a well-known compiler. In terms of compatibility with Java frameworks and libraries, it has a static system. 

Scala has a reputation for dealing with large databases and scalable solutions in enterprise-level applications. Its unique selling point is its ability to create large-scale, data-intensive applications.

Scala’s MLLIB library, which is supported by the well-known Apache Spark framework, provides competitive functionality. Thus, it’s capable of handling huge amounts of data. 

Using Spark and other big data tools and technologies enables developers to build, create, and deploy machine learning algorithms. Libraries like Aerosol, Saddle, and others are available in Scala that may aid in the development of scientific computing, linear algebra, and other applications involving random number generation. For data manipulation, these libraries include various options, including 2D data structures and automatic data alignment.

Even though Scala is rarely the first language of choice, Java veterans may find it quite simple to pick it up. As a result, finding a Java developer who is willing to learn is often preferable to searching for someone with the necessary skills from the outside.

#7 C/C++

Programming languages such as C/C++ are strong, adaptable, and widely used across the world. And there is no turning back when it comes to designing algorithms for machine learning. 

Languages like C, C++, Java, and Python have long dominated the developer community because they’ve been updated regularly to keep up with the newest developments.

Because these languages are considered low-level languages, computers can read and understand them. Machine learning programs can be installed on IoT devices since it is simple to give hardware-level functionalities. 

These kinds of applications benefit significantly from the speed at which they may be completed and delivered. Torch, TensorFlow, etc., are all C/C++ implementations of powerful libraries. Performance-critical applications have found them handy. 

C++ offers the ability to alter algorithms and do extensive memory management better than any other programming language. Developers can also fine-tune a wide range of performance settings using this language.

#8 TypeScript

Microsoft created TypeScript, an object-oriented programming language, in 2012. TypeScript is a JavaScript-based scripting language that supports JavaScript libraries from the beginning to the conclusion. 

JavaScript is used for large-scale application development. Kalimdor, a TypeScript-based browser-based machine learning library, is regarded as a viable option for constructing machine learning apps.

It is seen as a JavaScript-like language and set of tools, but with additional capabilities and tools. A TypeScript language, TypeScript Compiler, and a TypeScript Language Service are the three fundamental components of the language.

The primary benefit of utilizing TypeScript is a simplified version of JavaScript, making it simpler to understand and debug. Reading and interpreting the code gets a lot easier. Efficient development tools for JavaScript IDEs and various programming approaches are provided by it.

#9 Golang

Open-source, owned by Google, and light execution have made Go (Golang) a popular programming language. It can handle large amounts of data more efficiently by executing numerous activities simultaneously. 

Its ability to run in parallel is a big plus. An in-built vocabulary makes it a system-level language.

It’s one of GitHub’s fastest-growing languages, with a high degree of adoption across cloud computing providers. It is widely used in serverless computing architecture due to its closeness to C, garbage collection, and dynamic typing capabilities.

Go is a reasonably straightforward language to learn, thanks to its simple syntax and security features. Also, skilled Go developers are in high demand, to be hired.

#10 Shell

The Unix shell, a command-line interpreter, was the inspiration for the Shell programming language. 

Shell’s scripting languages and wrappers make it a great option for developing machine learning tools, algorithms, and apps because of its easy syntax.

Shell, a user interface for performing tasks, uses a specified language to gather and prepare using mathematical models. All operating systems, such as Windows, Linux, and Mac OS X, are supported by Shell.

To gather data, shell commands and scripts are used. It makes data processing simple and pleasant.

What Is The Most Widely Used Machine Learning Language?

Let’s start by taking a look at how prevalent machine learning languages are in general. While Python is widely used among data scientists and machine learning engineers, just 33% prioritize its use for development

It’s no surprise, considering the recent development in Python’s deep learning frameworks, including the introduction of TensorFlow and a broad range of additional libraries, such as PyTorch. 

Even though Python and R are often comparable, their popularity gap is steep. Only Python has a higher percentage of Python users than any other programming language, both in use (44%) and priority (44%). Overall, R is used by 31% of the population. Also, only 17% of developers, who use R, prioritize it as their primary programming language for machine learning.  

As a result, R is used mostly as a supplementary language rather than the primary one in most situations. Among the most popular languages, Python has the greatest percentage of users.

The Future of Machine Learning Is Bright!

There is no going back for machine learning services and solutions. New and interesting developments in machine learning and artificial intelligence are expected due to the availability of various programming languages and frameworks. 

If you’re looking to hire a developer for machine learning, you must have a clear understanding of your project. Your technology team must have a concrete goal and plan before you begin to look for developers.

Many beginner developers prefer Python as the ideal choice due to its extensive library and simplicity of use. So, it is easier to find them if your project demands so. 

Regardless of the language you choose, these are exciting times for machine learning. If you’re looking to hire full-time remote developers, contact us. Our team will help you find career-driven, full-time developers that are the perfect match for your industry, time zone, and company culture.

Vishal Srivastava

Vishal has played multiple roles over the past 8 years- a mechanical engineer, blogger, liberal arts student, & currently a marketing professional. Vishal's diverse experiences, particularly in the tech and marketing sectors, give him a unique perspective on emerging trends. Outside of his professional pursuits, he enjoys contemplating life in 2050, reflecting his forward-thinking approach and interest in future technological advancements.

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