Machine learning refers to any computer program that has the ability to learn from data, make decisions, and improve from those decisions over time. With machine learning and programming languages working hand-in-hand, it’s possible to build programs capable of complex tasks like image recognition and automated translation services. There are many different programming languages that specialize in machine learning, but this article will focus on the top ten programming languages used in today’s industry leaders in artificial intelligence.
Python
It is sure that python comes at the top when talking about machine learning and artificial intelligence. Python programming language is often described as a hybrid of high-level programming languages and scripting languages. It’s used frequently in data science and machine learning applications because it’s relatively easy to learn and use, while still being powerful enough to handle complex tasks. Python also comes with an extensive set of standard libraries that you can use to import data, manipulate files, perform calculations, and more. Many developers view Python as a gateway language into more advanced topics like machine learning because it provides several techniques that are commonly used in machine learning models. It’s also less strict than other languages about handling things like spaces around operators; instead of making you meticulously follow syntax rules, Python gives you some leeway so you can write your code more quickly.
Java
If you're looking to create artificial intelligence and need to do some heavy-duty calculations, there's no better language than Java. It's also popular because it supports object-oriented programming, which allows programmers to easily reuse code and build on top of existing systems. The only downside is that Java can be a little complex and verbose at times, so it may not be ideal for small tasks (such as those commonly performed by AI). But if you need a reliable solution that can scale to massive computing power, Java is your best bet.
R
If you're in a startup or building a product, chances are R will be your go-to programming language. After all, it's free and easy to learn. The open-source library offers hundreds of machine learning algorithms (most of which have been written by statisticians). There's even an active community that shares code and works to improve existing methods. Startups like Blossom use R as their go-to platform, but larger companies like Facebook use it as well. At its core, R allows users to implement machine learning models with multiple layers of complexity through its ggplot2 library and machine learning packages like nnet and glmnet.
C++
There are a lot of good reasons why C++ is one of the most popular languages to write machine learning code in. First, it has a great machine learning library: The libSVM C++ library. Second, it’s imperative to learn about pointers because many algorithms depend on them (though most people don’t want to think about that). Finally, if you want to read books from experts in machine learning and data mining using R, they will likely use C++ as their example language. Overall, even though you might be intimidated by how hard it is at first, learning how to program with C++ can increase your understanding of data science concepts and provide opportunities for future employment.
SQL
Before you can write or run any program, you need to store and manipulate data. You can do that with SQL. SQL stands for Structured Query Language and it is the standard language used to interact with most databases, including relational ones like MySQL, Oracle, and PostgreSQL. Since most machine learning algorithms are applied on top of structured datasets (like transactional records), it makes sense to learn how to work with structured data before tackling unstructured data (which will likely require natural language processing). Most programming languages have a library that allows you to use SQL commands—you just have to know them! In Python, these are called modules. In R, they are referred to as packages.
Scala
Despite its reputation, Scala is a more approachable language than many of its peers. Scala is extremely flexible and ideal for use with machine learning due to its ability to easily create new classes or reuse classes from other languages. It’s also one of only a few mainstream languages that support functional programming constructs out of the box, which may make it easier to approach for programmers who are primarily used to languages such as Python or Java. Not all machine learning algorithms perform well with Scala, however; if you’re looking at developing an algorithm-heavy solution, it might be worth sticking with Python or C++ instead.
JavaScript
The popularity of JavaScript stems largely from its position as a language that’s both easy to learn and flexible enough to provide ample room for experienced programmers to grow. For instance, companies like Netflix use open-source toolkits such as React Native and Polymer to create dynamic apps that are compatible with smartphones, tablets, and desktop machines. Beyond programming languages, JavaScript also has machine learning capabilities; it can be used to train algorithms on large data sets in order to develop predictive tools or perform predictive analytics.
Julia
An open-source alternative to many popular languages like Python and R, Julia has a wide variety of libraries available. This means that you don’t have to spend as much time developing tooling. It also offers fast execution times through its Just-In-Time compiler and flexible syntax that can help you write more readable code. While it has great potential, Julia is still relatively new in comparison to some other languages so you might find that there isn’t a big support community just yet; however, if you’re looking for a flexible language that compiles quickly, it may be worth considering.
Clojure
Many new languages have popped up as solutions to Python’s computational and syntactical limitations, but few have made as much of an impact on recent data science as Clojure. Although it was created with a functional programming ethos in mind, Clojure is surprisingly flexible and can be used to build machine learning applications in a number of ways.
Go
For those of you who have been paying attention, Go is everywhere. It's powering Google search, and it's embedded in most Google services (including YouTube). It's being used at top technology companies like SoundCloud, Digital Ocean, and Walmart Labs. And let's not forget that Netflix built a small Hadoop framework using Go -- yes, really. Go has an active community of core contributors and more than 170 third-party libraries. While it might not be able to replace Python or R in machine learning workflows just yet, there are a lot of reasons why data scientists should be taking notice of what could end up being one of 2017's breakout technologies.