The Best Programming Languages for Machine Learning

Attend any high-profile tech networking event, and you’ll notice one thing: everyone’s talking about machine learning (ML). For good reason: it’s evolving, exciting, and is truly the next frontier where programmers and data scientists are redefining our world. 

And maybe you’re just as enthused about it as we are, but you realize you need to take the first step and learn ML. Well, we can help. From ML algorithms to neural network development, from sentiment analysis to decision trees, the ins and outs of machine learning programming languages are fascinating, and mastering them can help you launch a fruitful career in tech.

So let’s dive in. Here are the best programming languages for machine learning. 

What is machine learning?

To make sure we start out on the same page, let’s quickly define what exactly we’re talking about, because there’s a lot of noise surrounding ML.

Here is a list of the seven best languages for machine learning.

1. Python

‍What is Python?

Unambiguously, Python_{Is Programming Hard? Make It Easy with Python} is the best language for machine learning. It’s an interpreted language that is easy to read and understand, as its syntax is relatively similar to spoken languages. Because of this, it is also one of the easiest languages to learn. It is versatile, powerful, and has wide support for numerous niche fields within programming, machine learning being one of the most well-supported thanks to the abundance of ML libraries. 

And if you find yourself wooed away from machine learning by some other field, your expertise in Python will still be applicable; it’s used in web development, data analysis, and much, much more. We can’t recommend learning this language enough.

How is Python used in machine learning?

Frankly, think of a machine learning application, and you’ll probably find Python being used. It’s great when it comes to deep learning and neural networks thanks to libraries such as Keras_{A Beginner’s Guide to Deep Learning: Why Data Scientists Use Keras} and PyTorch. Then, you can find it throughout natural language processing and sentiment analysis because of other Python libraries such as Natural Language Toolkit, spaCy, and TextBlob. 

But beyond just having robust libraries that can add further capabilities to the language, Python in and of itself is a powerful machine learning language. For example, it can easily integrate with databases. And if you’re going to be training models on data, you’re going to need a lot of it, so interfacing with where that data lives will be key. Out of the box, Python’s capabilities allow for this without any hassle.

If you remember one piece of tech career advice from this article, let it be this: Learn Python.

2. R

What is R?

R is an open-source language that really shines when it comes to mathematical operations, specifically in its robust stats capabilities. It’s popular in academia, but its applications within tech and data science have made it attractive as a machine learning language in recent years. It excels at data manipulation and visualization, something prized by both machine learning engineers and academics alike.

One thing to note is that this language is easy to grasp at a basic level, but it has a steep learning curve. If you end up in a more Python-focused ML role, then you might not encounter this sudden ramp-up in complexity, but if you want to get deep into all of its myriad capabilities, you should prepare yourself for fairly heavy learning.

How is R used in machine learning?

R’s unique capabilities in stats make it a top choice when it comes to evaluating ML models. Since a model builds its own logic to solve problems, it needs to apply powerful and incisive statistical techniques such as linear regression to compare its outcomes against its goals. This continuous model refinement is the backbone of numerous ML algorithms, and it’s all powered by stats, making R a key tool. In addition, R’s packages such as caret and glmnet expand what the language can do with statistics, meaning they’re often paired with this language.

But this language isn’t just about stats; it has other capabilities as well. You can use it to prepare data, for example. That means that it can assist in the cleaning and unification of datasets. After all, raw data is remarkably noisy. By applying R libraries such as dplyr and tidyr, data can be made usable, and new, clean algorithms can be born.

3. Java

What is Java?

Java_{Java as a Bridge to a New Profession} is one of the most widely used languages in the world. It is object-oriented, meaning it makes code easier to organize and reuse. In addition, it’s versatile — you can write code just once, and it can deploy on any platform running a compatible Java virtual machine. Plus, it has security built-in thanks to its automatic memory management that takes the form of its garbage collection.

We could keep listing features, but the core thing to understand about Java is that it is reliable and applicable to a wide range of domains — web development, server-side programming, and enterprise applications included. It has a wide community, and its broad adoption has helped make it applicable across diverse domains.

How is Java used in machine learning?

In terms of machine learning programming languages, this might be a surprising entry for people who’ve been looking into the subject, but machine learning is about so much more than the algorithms themselves. The algorithms need to be deployed and need to play well within enterprise application environments — Java is key here. 

In addition, in situations where real-time analysis is crucial, such as in finance, the scalability and multithreading capabilities offered by Java make it a great choice. Because it has the ability to process huge amounts of data quickly, it’s ideal for algorithms that demand rapid data input. It can help these models produce immediate, accurate, and reliable results.

4. JavaScript

What is JavaScript?

JavaScript is one of the easier languages to learn, and it is the prime language for web development. That’s because its main capabilities focus on making web pages dynamic, interactive, and attractive. It works on all major web browsers, and while each browser has its own unique quirks and specific ways of displaying pages, additional libraries have developed over time to ensure that, after you spend your time writing code to make the site shine, it’ll look just as you intended regardless of the browser.

In short, it is the language for web devs. When it comes to front-end programming_{All about Front End: What is It, How It Works, and Why It Worth Your Time}, there’s no better language.

How is JavaScript used in machine learning?

You might have noticed that we didn’t really talk about anything that would seem to indicate that this could even approach the title of “best language for machine learning,” but hold on, there are numerous reasons it’s here.

First, not all machine learning models run on servers. In fact, many of them run directly in browsers. And the language that works best when it comes to interactions with browsers? Yup, JavaScript. Of course, it also has extra ML capabilities thanks to libraries such as TensorFlow.js and Brain.js.

But chatbots also integrate with JavaScript. These interactive natural language processing and generation tools that you can find popping up all over the web often rely on the language.

5. Julia

What is Julia?

Compared to the previous entries, this open-source machine learning language is fairly young. In fact, it came out just over a decade ago. It was built to marry the ease-of-use of languages such as Python with the performance of more complex languages such as C. Because of this, it is known for being impressive when it comes to performance while still being easy to write and understand. But beyond those core features, it is also interoperable, meaning it plays well with C and Python, and it assumes you’re going to be adding packages, so it comes with a built-in package manager. While Python and R are still the core machine learning languages, Julia is rapidly gaining popularity.

How is Julia used in machine learning?

Even from that definition, you can likely see why so many people consider this to be the best language for machine learning. Its applications abound. For example, since its performance is so top-notch, it is often used when machine learning engineers need to tackle computationally intensive tasks like processing massive datasets. And we’re talking massive.

Because of its power and simplicity, it is also great for experimentation. If you need to tinker with a model, Julia’s user-friendly ethos means you can prototype quickly and clearly without needing to wrestle with difficult syntax. This makes the language especially appreciated among data pros both in academia and in the tech industry.

As you might be able to tell, we’re excited about the promise that Julia represents. It’s already gaining great traction, and it’s only getting more and more effective thanks to the vibrant ecosystem of language-specific libraries cropping up around it.

6. LISP

What is LISP?

As a “machine learning language,” this is kind of a sneaky entry, since strictly speaking, this is not just one language, but rather a family of programming languages. And while Julia is the youngest language on this list, LISP is the oldest, having first come out in the 1950’s. As you might imagine from that fun fact, it is not the most widely used language for modern machine learning programs, but it is nonetheless useful for AI, specifically because of its capabilities in symbolic reasoning.

How is LISP used in machine learning?

While indeed more a historic language, many companies do still use LISP. Specifically, they tend to focus on a dialect of the language known as Common Lisp.

Within this dialect, one common task is the development of expert systems, AI bots that can emulate human decision-making capabilities. These systems rely heavily on an approach that emphasizes symbolic reasoning. Since its first release over 70 years ago, this language has applied this feature to focus on creating effective AI.

For example, symbolic reasoning is crucial when it comes to natural language processing. In order to understand and recreate the deep nuances of human communication, AI must be able to comprehend input and generate output at a level that can be hard to realize with more restrictive logic flows. 

7. C++

What is C++?

C++ is what is known as a mid-level language. That means it is closer to digital logic than Python or JavaScript, two coding languages that make programming simpler by more closely emulating the logic of spoken languages.

It is a general-purpose language that allows for manual memory management, object-oriented programming, and templates. That’s all to say that it is versatile, robust, and that it sits right between lower-level digital languages that are difficult to grasp but powerful and higher-level languages that are easier to understand but sacrifice a bit on performance.

It is widely used, so it has applications in numerous industries and fields in addition to machine learning.

How is C++ used in machine learning?

As a machine learning language, C++ has two realizations that we find exciting, but keep in mind that there are still additional places in which the language meshes with machine learning.

First of all, C++ is often the language used on devices that are short on computing resources. Instead of deploying on servers or websites, it is often used in embedded systems, i.e., as the code for chips found in small devices, or as the basis for internet of things tech. That means that if you want onboard machine learning in your new smart gadget, you’re going to likely be using C++.

But it’s also crucial when merging machine learning with game development. Because of its mid-level versatility, games are often coded in C++. That means that integrating machine learning models into games requires C++. These models can inform enemy AI behavior, create the procedurally-generated content that is at the core of so many modern games, or even help with animation.

How to choose the right machine learning language

When it comes to picking among the myriad programming languages for machine learning, you can either see the abundance as an opportunity or a challenge. So, here’s a little extra advice. First, learn Python. We said it before, we’ll say it again — it is by far the most widely-used language. From developing complex machine learning algorithms to helping data analysts sort through noisy data sets, it’s the way to go.

But then, it’s always a good idea to have an additional language. And that really depends on what intrigues you most. Want to help develop and implement a chatbot? Then JavaScript might be right for you. Looking to really explore the math side of the equation? Try R. Want to help make a virtual orc come up with just the right taunt to inspire an epic battle? Go for C++. Any of these would be a great language for machine learning — it’s just about the career path that excites you most.

Do I need to be an expert in programming to land a machine learning job?

The answer to the question posed is no, you don’t need to be a programming expert to get a job in machine learning. But you’re going to need some skills.

The good thing is you can gain those skills fairly quickly. In only 8 months, our Data Science Bootcamp can help you acquire the machine-learning know-how that will land you a job. And you don’t have to just take our word for it — check out the story from Gor_{Changing His Career, Not His Company: Gor Mikayelyan’s TripleTen Story}, our grad who is now applying his machine learning skills in a job at Amazon.

Here's another inspiring success story from one of our students who achieved their goal of becoming a Machine Learning Engineer.