Best Programming Languages for Machine Learning: Top 10 List

Programming is a pivotal aspect of Machine Learning. After all, ML applications and ML algorithms are written and designed using programming languages. However, there’s often much confusion surrounding the question, “what are the best programming languages for Machine Learning?

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Without further ado, let’s dive into the list of the top ten programming languages for ML!

Python

Python is extremely popular in the developer and coding community. It is a highly dynamic, open-source language that supports object-oriented, imperative, functional, as well as procedural development paradigms.

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Python comes with an assortment of excellent libraries and tools for ML, including Scikit Learn, TensorFlow, ChatterBot, and much more.

C++

One of the oldest programming languages, C++ is highly suited for Machine Learning, thanks to its ML repositories like TensorFlow, LightGBM, and Turi Create. Speed and efficiency are the two key aspects of C++. Thus, if implemented correctly, C++ can help create fast and well-coded algorithms.

Furthermore, C++ allows you to implement advanced computer vision and ML systems from ground-up. It also comes with numerous other low-level features like the choice of the memory management system.

JavaScript

JavaScript is a very most popular web scripting language. It has numerous libraries for training and deploying ML models including TensorFlow.js, Brain.js, stdlib-js, machinelearn.js, math.js, face-api.js, and R-js.

Thanks to these wonderful ML libraries, JavaScript has now found applications in enterprise-level and personal projects.

Java

Java is one of the most extensively used programming languages for developing Big Data ecosystems and also for software development. In fact, large corporations (both in the public and private sector) have a massive Java codebase that leverages JVM as the primary computing environment.

However, that’s not all. Java also has a host of ML libraries like Weka, ADAMS, JavaML, Mahout, RapidMiner, Neuroph, JSTAT, DL4J, to name a few.

C# is a general, flexible, and open-source, object-oriented programming (OOP) language primarily used for web development and networking. It is a versatile language as it allows developers to build varied applications like web apps, mobile apps, consoles, and even backend frameworks.

As for Machine Learning, C# has a dedicated .NET Core machine learning platform – ML.NET. ML.NET is a cross-platform, open-source ML framework that allows .NET developers to work on ML applications. Apart from this, it also has Accord.NET and ML-Agents.

Julia

Julia is the perfect match for ML developers who are always on the lookout for languages that will allow them to write ML algorithms as code. It was created to cater to the need for high-performance numerical model analysis essential for ML applications, and hence, it is highly suitable for Machine Learning.

Julia Computing maintains that Julia has the best-in-class support for ML frameworks such as TensorFlow and MXNet, which makes the adaption to existing workflows much more manageable. While Julia’s mathematical syntax allows you to express algorithms as you would on a paper, Flux converts the code into trainable models with automatic differentiation, GPU acceleration, and support for large datasets through JuliaDB.

Shell

Just like Python, Shell features a simple and neat syntax. Hence, it is a beginner-friendly option for those who wish to explore the basics of ML app development. Another great aspect of Shell is its speed – what would take over ten minutes to achieve via a graphical interface, Shell can accomplish in one minute!

It has some very high-rated ML libraries including MI-Notebook, DI-Machine, and Docker-predictionio.

R is a dynamic, array-based, multi-paradigm language. It supports object-oriented, imperative, functional, procedural, and reflective programming paradigms. The reason why R has gained popularity among Data Scientists and developers is its capacity for statistics and data visualization.

R has support for Linux, OS X, and Windows operating systems. Plus, it comes with GNU bundles (great for ML applications). Apart from creating ML algorithms using R, you can also design statistical visualizations for the same with R studio. ML_for_Hackers, Machine Learning in R, and Benchm-ml are some excellent ML repositories in R.

9. TypeScript

Just like C++ and Java, TypeScript is a strongly typed, compiled, OOP language. Microsoft developed it in 2012 as a typed superset of JavaScript compiled to JavaScript. According to GitHub’s Octoverse report, TypeScript is one of the fastest-growing languages.

TypeScript is being leveraged for ML applications through Kalimdor – a browser-based Machine Learning library written in TypeScript. Kalimdor can run directly on browsers (like Python’s Scikit-Learn). Guess.js and machinelearn.js are the top-two ML repositories of TypeScript.

Scala

Scala is a type-safe JVM language that combines the aspects of object-oriented and functional programming languages. This combination is what makes Scala a highly concise and logical programming language. Since Scala uses JVM in runtime, it performs way faster than Python. Hence, it is becoming increasingly popular in Data Science and Machine Learning communities.

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The top two ML libraries in Scala are Aerosolve and BIDMach.

While these are the top ten popular programming languages for ML, you must choose the language that best suits your current situation. For instance, if you are a beginner, Python would be the clear choice due to its simple syntax and easy learning curve. However, if you have some experience in the development domain, you could experiment with these languages – choose what goes best with your development needs. Lastly, remember that each comes with its unique advantages for Machine Learning – so, use them wisely!

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Frequently Asked Questions (FAQs)

1. Do we need to master programming to learn machine learning?

The extent of programming skills needed to understand machine learning is determined by how you want to utilize it. If you're going to use machine learning models to solve real-world business issues, you'll need programming experience, but math and statistics skills will do if you want to grasp the fundamentals. It all depends on how you want to use machine learning to its full potential. To be exact, ML models require a solid grasp of programming foundations, techniques, data structures, memory allocation, and reasoning. Anybody with a basic understanding of programming may easily begin a career in machine learning as several programming languages provide many built-in machine learning libraries for machine learning.

2. Why do companies use R programming for machine learning?

As R is an open-source programming language, it offers a low-cost option for machine learning projects of any scale. Machine learning professionals will have to utilize the NumPy module in Python programming language, which is a clumsier implementation when compared to R. R enables the natural implementation of matrix arithmetic and other data structures like vectors. Because of the variety of machine learning techniques it offers, R is a popular option for machine learning. It has a wide range of tools to provide, including data visualization, data sampling, data analysis, model assessment, and supervised/unsupervised machine learning. The R programming language has a simple programming style and is a powerful programming language that is also cross-platform compatible.

3. What is Julia programming language?

Julia is a strong, general-purpose, high-level programming language that is gaining traction as a possible rival to Python and R, focusing on machine learning. Even though it is a general-purpose programming language that Machine learners can use to create a wide range of applications, it is best suited to high-performance numerical analysis and computational research. Julia is helping organizations in making use of machine learning. The LLVM framework is used to create Julia programs, and it is compiled on just-in-time or at run time. Julia allows machine learning professionals to work quickly without relying on manual profiling or optimization approaches to solve all performance issues.

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