If you are just getting started in the field of machine learning (ML), or if you are looking to refresh your skills, you might wonder which is the best language to use. Choosing the right machine learning language can be difficult, especially since there are so many great options.
There are an incredible 700+ programming languages in widespread use, and each has its own pros and cons. If you are just beginning your career as a machine learning engineer, with time you’ll discover which are the best programming languages for the specific business problems you are trying to solve.
Before diving into the best machine learning languages, let’s explore the concept.
What is Machine Learning?
Without going into too much detail, machine learning is a subset of artificial intelligence that provides computer systems with the ability to automatically learn and make predictions based on data. These predictions can vary greatly depending on the specific use case.
In the field of machine learning, a machine learning specialist doesn’t have to write out all the steps necessary to solve a problem because the computer is capable of “learning” by analyzing patterns within the data. The model can then generalize the patterns to new data.
For further reading on machine learning, I recommend you take a look at our article “What is Machine Learning?”
Most Popular Machine Learning Language: Python
Before diving into the different machine learning languages, it’s important to recognize that there is not really one “best” language. Each one has its own pros, cons, and specific capabilities. It largely depends on what you’re trying to build and your background.
With that said, the most popular machine learning language, without a doubt, is Python. Around 57% of data scientists and machine learning developers rely on Python, and 33% prioritize it for development.
Python’s frameworks have greatly evolved over the past few years, which has increased its capabilities with deep learning. There has been the release of top libraries like TensorFlow and various others.
Over 8.2 million developers across the globe rely on Python for coding, and there’s a good reason for that. It is a favorite choice for data analytics, data science, machine learning, and AI. Its vast library ecosystem enables machine learning practitioners to access, handle, transform, and process data with ease. It also offers platform independence, less complexity, and better readability.
The built-in libraries and packages provide base-level code, which means machine learning engineers don’t have to start writing from scratch. And since machine learning requires continuous data processing, Python’s in-build libraries and packages assist with almost every task. All of this leads to a reduced development time and an improvement in productivity when working with complex machine learning applications.
Some of the world’s biggest tech giants like Google, Instagram, Facebook, Dropbox, Netflix, Walt Disney, YouTube, Uber, and Amazon prefer Python as their programming language.
While Python clearly stands out as the most popular language, there are various others that must be considered. The running five are Python, R, C/C++, Java, and JavaScript. The distant second to Python is usually considered to be C/C++. Java is close behind, and while Python is often compared to R, they really don’t compete in terms of popularity. In surveys involving data scientists, R has often achieved the lowest prioritization-to-usage ratio among the five languages. Javascript is often placed on the lower end of the list.
While nowhere near as popular as the top five, there are various other languages that machine learning practitioners use and are worth consideration, such as Julia, Scala, Ruby, MATLAB, Octave, and SAS.
Choosing Based on Your Application
When choosing the best language for machine learning, the most important factor is to consider the type of project you’ll be working on, or your specific applications.
If you are looking to work on sentiment analysis, your best bet would likely be Python or R, while other areas like network security and fraud detection would benefit more from Java. One of the reasons behind this is that network security and fraud detection algorithms are often used by large organizations, and these are usually the same ones where Java is preferred for internal development teams.
When it comes to less enterprise-focused areas like natural language processing (NLP) and sentiment analysis, Python offers an easier and faster solution for algorithm building thanks to its large collection of specialized libraries.
As for C/C++, the language is often used for artificial intelligence in gaming and robot locomotion. The machine learning language offers a high level of control, performance, and efficiency as a result of its highly sophisticated AI libraries.
R begins to make its presence known in the areas of bioengineering and bioinformatics, and it has long been used in biomedical statistics inside and outside academia. But if we’re talking about developers new to data science and machine learning, JavaScript is often preferred.
Language is Secondary to Skills
When entering the world of machine learning and choosing which language to use, it’s important to recognize that the language you learn is secondary to mastering basic machine learning concepts. In other works, you’ll need to cultivate core data analytics skills.
If you don’t have a fundamental knowledge of statistics, deep learning, systems process, and design, it will be really difficult to choose the right models or solve complex machine learning problems.
If you’re new to data analytics and machine learning, then Python should be at the top of your list. As we’ve discussed, Python is syntactically straightforward and easier to learn than other languages. But if you’re already an experienced programmer with years of experience under your belt, specifically experience with a certain language, then it might be a better option to stick with what you already know.
There are some essential machine learning skills that will make it easier to choose a language. Some of these skills include software engineering skills, data science skills, deep learning skills, dynamic programming, and audio and video processing.
If your professional background is heavily involved with data science, it’s likely better to prioritize Python. The most popular machine learning language is heavily integrated with data science, which is why it has become data scientists’ go-to language. But if your background involves data analysis and statistics, R is heavily tailored to you.
Front-end developers often have existing experience with JavaScript, making it easier to extend its use to machine learning. Computing hardware and electronics engineers often choose C/C++ over the other languages and specifically avoid JavaScript, Java, and R.
The lesser popular language, Java, is prioritized by front-end desktop application developers given its efficiency with enterprise-focused applications. If you are working for a big enterprise, the company might even tell you to learn Java. It is less common for beginners embarking on the machine learning journey to choose Java themselves.
As you can see from this article, there is a lot that goes into choosing the best language for machine learning. It’s not as simple as one being the “best.” It all depends on your experience, professional background, and applications. But popular languages like Python, C++, Java, and R should always be considered first.
