What’s the Best Language for Machine Learning?

Will Hillier

In this post, we look at some of the most common programming languages used in the field of machine learning. Which one is right for you?

A subset of artificial intelligence, machine learning is a complex but exciting field. Many data experts dedicate their careers to mastering it. If you’re new to data analytics or data science and have an interest in machine learning, there are some particular skills you’ll need to develop. Besides theoretical knowledge, this includes some basic understanding of programming.

But with hundreds of languages available, which is the best one for machine learning? In this post, we’ll explore what machine learning involves, before looking at some programming languages you might want to consider adding to your repertoire. We’ll cover:

  1. What is machine learning?
  2. What skills are important for machine learning?
  3. Five of the top machine learning programming languages right now
  4. In summary

First up: what is machine learning?

1. What is machine learning?

Machine learning (ML) is the study of computer algorithms that learn without being explicitly programmed by humans. It is a subset of artificial intelligence (AI). Although ML algorithms start with basic instructions from their human designers, they learn and make predictions on their own. They achieve this by ingesting training data, which helps them to identify patterns and trends. This information can be used in a broad variety of ways, as we’ll see.

Learn more: What’s the difference between machine learning and deep learning?

When do we use machine learning?

We use machine learning in cases when it’s not practical for humans to create specific algorithms. This is usually because there’s so much data to work through that it would take a person countless lifetimes to do the job manually! With big data flooding our lives, machine learning is an increasing necessity. But how does it work in practice?

For starters, let’s look at the field of natural language processing (NLP). This is where algorithms learn to understand the contextual nuances of human language. NLP has applications from language translation to internet search. Email providers even use it for spam filtering.

Machine learning is also used for computer vision. This is where algorithms ingest digital images or video to make sense of those data. This can help in areas like medicine, to diagnose patients based on their scans. By analyzing visual data, we can also program the navigation systems in autonomous vehicles, like self-driving cars or military drones.

From credit card fraud to solving complex mathematical problems, machine learning has countless uses. In short, it is a huge part of the world we live in, and it is growing. If you’re considering a career in machine learning, now’s a great time to dip your toe in!

2. What skills are important for machine learning?

If you’re entering the world of machine learning, you’ll need to cultivate some core data analytics skills. This includes knowledge of at least one programming language. Machine learning involves manipulating data in very specific ways. You’ll need to prototype algorithms and understand the internal mechanisms behind ML concepts. Programming is integral to this. Machine learning engineers probably spend more time writing code than developing statistical models. And to communicate with computers, we need at least basic coding skills.

However, the language you learn is secondary to mastering basic machine learning concepts. Without a fundamental knowledge of statistics, deep learning, systems process and design (and so on) you’ll never know how to choose the right models or solve ML problems. So put machine learning theory at the top of your to-do list. However, presuming you’re well underway with this, where next for your programming skills?

If you’re new to data analytics and machine learning, consider learning a language like Python. Python is syntactically straightforward and easy to learn. If you’re already an experienced programmer with years of experience in say, C++, it might be better to stick with what you know. The truth is that there is no one ‘correct’ language to learn for machine learning. But there are some languages that are more in vogue than others. Let’s look at some of these next.

3. Five of the top machine learning programming languages

In 2019, GitHub surveyed the top ten machine learning programming languages on their platform. From R to Java and C++, we’ve selected five of our favorites to explore more closely.

1. R for machine learning

What is R?

R is a functional programming language often used for data analysis and visualizations. It’s popular with scientists, statisticians, and others in the academic community. Derived from an older language, S, it was first developed in the early 90s at Auckland University in New Zealand. It has since grown and now includes support for object-oriented programming (a design principle that is important for, amongst other things, machine learning).

The fact that R is so popular with statisticians explains, in part, why it’s also so popular in the ML community. One of R’s main strengths is its large number of user-created extension packages, which allow users to apply specialized statistical techniques. There are currently over 15,000 packages available on the Comprehensive R Archive Network (CRAN).

How is R used in machine learning?

In machine learning, R is often used as a supplementary tool to support other languages. However, it’s also popular in its own right for tasks like sentiment analysis. R is commonly used in scientific fields like bioengineering (designing and testing medical equipment), bioinformatics (the study of large amounts of biological data), and ecology. But it is well-suited to any machine learning task that is heavy on statistics.

There are many R packages designed to streamline data-heavy machine learning tasks. For instance, the Classification and Regression Training (caret) package makes creating predictive models far easier. Randomforest can create random forest algorithms using decision trees. Meanwhile, packages like ggplot2 and plotly are excellent for data viz.

2. C++ for machine learning

What is C++?

C++ is an object-oriented, general-purpose programming language. Launched in the 1980s as a systems language (for building system architectures) it is complex to learn but has proven popular for performance-critical jobs. It’s now used to create desktop applications, video games and even to program Martian space rovers. Pretty cool!

C++ has many applications largely because it is a low-level language. This means it communicates with machines close to their native code. (The alternative is a high-level, abstract language, like Python, which is easier to use but slower to execute). Being low level, C++ has a steep learning curve. But it is also excellent for memory manipulation. Speed here is key.

How is C++ used in machine learning?

In terms of machine learning, C++ users can manipulate algorithms and manage memory resources at a granular level. That’s why it lends itself so well to areas like AI, where speed is critical for analyzing large datasets. The trade-off is that C++ is not great for quick prototyping. Even so, it remains a top favorite among data analysts and machine learning engineers.

Because C++ offers close control over performance, it’s popular in areas like robotics and gaming, which need high responsiveness. These are also areas where machine learning is growing fast. What’s more, C++ has several sophisticated artificial intelligence and machine learning libraries. These include the deep learning framework, Caffe, the neural network library, DyNet, and Shogun, an open-source ML library with lots of different models to play with.

3. Java for machine learning

What is Java?

Like C++, Java is an object-oriented language. Its syntax is similarly complex to C++, although it doesn’t work at such a low level. Java is also a general-purpose programming language. It’s used to create applications that run on any platform, via the Java Virtual Machine (a kind of system emulator). It’s commonly used to create applets for web pages, large-scale enterprise systems, and apps on the Android mobile platform.

Java has a long history in the professional sphere. Its users traditionally worked in financial institutions and the enterprise industry. It’s now often used in areas like network/ cybersecurity and fraud detection. Many who use Java for machine learning do so because they’re used to applying it on enterprise development projects.

How is Java used in machine learning?

Java is highly scalable. This makes it great for creating complex, large-scale ML algorithms. Many big data frameworks like Hadoop, Hive, and Spark (used for ML) are also Java-based. The Java Virtual Machine allows users to create ML tools fast and roll them out at speed. It’s also quick to execute. For all these reasons, tech giants like Twitter, LinkedIn, and Facebook all use Java to manage big data.

Java also has several machine learning libraries and tools. Weka, for instance, is a Java workbench used for data mining, analysis, predictive modeling, and visualization. The Massive Online Analysis (MOA) framework is used for data stream mining and contains ML algorithms for things like classification, regression, clustering, and more. You can learn more about classification and regression for predictive analytics in this post.

4. JavaScript for machine learning

What is JavaScript?

Unlike the languages we’ve looked at so far, JavaScript is a high-level language. This means that its syntax is much simpler to grasp, making it easier to learn. JavaScript was originally designed as a scripting language. It has since evolved into a general-purpose programming language that you’ll commonly find running in your browser, in the form of things like pop-up messages or live clocks.

While JavaScript is great for front-end work, it also runs server-side. Here, it is often used as an API for apps built using languages that may lack JavaScript’s high-level functionality. And in case you were wondering, no: JavaScript is no relation to Java! They have different design principles and are maintained by different organizations. But it was worth asking!

How is JavaScript used in machine learning?

While JavaScript lacks the speed of lower-level languages, its web applications are useful for ML. For instance, developers often funnel output from machine learning algorithms into Java-based web dashboards. However, it is less well-suited to labor-intensive tasks. While ML often needs to crunch complex numbers, JavaScript contains fairly basic mathematical functionality.

With that said, the number of JavaScript libraries for machine learning is growing. For instance, math.js provides the language with far more mathematical flexibility and computing power. It also now supports packages built for other languages likeTensorFlow.js (originally made for Python). This allows JavaScript developers to run/ retrain existing ML models, and to create new ones. It also boasts neural network libraries, like Synaptic (which emulates the functionality of the brain) and image processing tools like OpenCV.js. Watch this space, because JavaScript isn’t going away!

5. Python for machine learning

What is Python?

Last but not least, Python. A high-level, general-purpose programming language, Python is an easy one to learn. Its popularity has boomed in recent years, taking it ahead of C++ in fields like data analytics and machine learning. Python’s straightforward syntax and speed to competence make it excellent to learn and great for fast prototyping.

As a high-level language (like JavaScript) Python executes more slowly than some languages. It makes up for this with a huge array of libraries for everything from gaming and special effects to data analytics, AI, and ML. Even if you already use a more complex language, Python is still worth learning. Its agility and ubiquitous nature mean it’s a string well worth adding to your bow. If you’re keen to add this language to your repertoire, check out our top tips for learning Python.

How is Python used in machine learning?

In machine learning, Python has similar applications to Java. However, it is often used in more scientific, less-enterprise-focused areas, e.g. sentiment analysis and natural language processing. Python’s recent surge in popularity is closely linked to the fact that it has evolved alongside the field of data science. They are now almost symbiotic as a result.

Python’s standout feature is the Python Package Index. This contains thousands of libraries of code, many of which have been specifically created for machine learning.TensorFlow allows beginners and experts alike to train ML algorithms with minimal effort. Keras is a popular neural network library, while NLTK (short for Natural Language Toolkit) is great for working with language data. While Python is not the fastest language to execute, for those interested in scientific computing and ML, it is the gold standard.

4. In summary

In this post, we’ve explored the importance of programming in the field of machine learning. We’ve learned that, while there’s no single ‘best’ language to learn, some are more suited to machine learning tasks than others. We now know that:

  • Machine learning is the study of computer algorithms that learn without human input.
  • ML has countless applications, from natural language processing to computer vision, neural networks, predictive analytics, and more.
  • Lower-level languages (like R, C++, or Java) offer greater speed but are harder to learn.
  • Higher-level languages (like JavaScript and Python) are easier to use but slower to execute.
  • Python is a key language for machine learning and data analytics. For speed-to-competence and breadth of application, it’s probably the best one for beginners.
  • Nevertheless, the right language hinges on the problem you’re solving, your expertise, and your programming experience. Don’t limit yourself!

Machine learning is just one of many exciting career paths you could pursue once you have a foundation in data analytics. If you’re brand new to the field, try out our free, introductory data analytics short course. And, for further reading, check out the following:

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