​So you’re thinking about incorporating machine learning into your company’s processes to make them more efficient. Or perhaps, you’re thinking of learning a programming language so you can get into machine learning. In either event, you’ve made a great choice. Machines can learn much faster than humans for certain tasks. If you take machine learning a step further and apply it to physical robotics, machines can build goods and provide services faster. Before you begin, you’ll want to decide upon one of the best languages for machine learning.

​Best Languages For Machine Learning FAQ

Machine Learning

Image by Gordon Johnson from Pixabay

​The applications for machine learning are tremendous, and the realm of academics is even more vast regarding the ways you can train computers to execute various functions. This can make choosing the best machine learning language to study rather difficult since there are many useful machine learning languages that are regularly used in practical, real-world situations. If you operate a computing, manufacturing, or services related firm that uses some form of machine learning, your choices can also be complicated. We will answer some commonly asked questions about which language might be best for you.

​1. Which Is the Best Machine Learning Language?

​2. What Do the Best Languages for Machine Learning Do?

​3. How Does Machine Learning Work?

​4. Where Can I Get an Education in Machine Learning?

​5. Have More Questions about the Best Languages for Machine Learning?

​How We Reviewed

​We chose the best languages for machine learning based upon their breadth of use among top companies, but we also considered which have the greatest capacity to learn and expand the existing offerings of the machine-learning field. That’s because we know you will want your business to have the edge over the competition, so we wanted to help you by pointing out languages that can build your foundational programming, and that will give you the greatest latitude of flexibility to expand.

​Overall Price Range of Developing in the Best Languages for Machine Learning

Machine Learning

Image by Pete Linforth from Pixabay 

​Except for Ruby, Java, and Javascript, all the best languages for machine learning we’ve reviewed are free to develop in, or at least open-source, thanks to the availability of integrated development environments for all the languages here. The bully here, threatening the open and free nature of the three aforementioned exceptions, appears to be Oracle. Because Oracle has ownership rights of Ruby, Java, and Javascript, at least at the trademark level, it can legally do what it wants.

And perhaps it should be able to; who are we to judge what a private company does with its intellectual property? The bottom line for the price range is that it’s generally free to learn how to program in any of these languages (minus any necessary training, education, books, etc.) because you can experiment with the programming language and even develop in any of them for free (to a degree). Oracle is hinting tentatively that even the ability to try out Java for free at the personal and individual level may end within the coming years.

​What We Reviewed

  • ​Java
  • ​Python
  • ​R
  • ​JavaScript
  • ​C
  • ​C++
  • ​Swift
  • ​Ruby
  • ​Scala
  • ​Julia

Features

So, Java is kind of a big deal. If you use an Android, your phone depends on it. And Java helped pioneer a new method of programming. From its humble beginnings as a pet project of a company then called Sun Microsystems to build a better television remote, it exploded in popularity as one of the first programming languages to allow Web surfers to see something other than static HTML pages in their browsers.

​Java has given way to the much-simplified Javascript as a platform and programming language for applications that appear within your Web browser, Java has thrived in its evolved form as one of the top artificial intelligence programming languages. Now owned by Oracle, Java’s claim to fame was its Java Virtual Machine.

Unlike the other historical programming languages, Java’s “applets,” arguably a precursor to the marketing term “app,” or mini-application on your mobile device, could run on any computer that might browse the Web, regardless of the processor that computer was running. This is because Java’s various virtual machines were built to run Java code on whichever device you were using to surf the Web.

Independence from any machine or platform by use of the “virtual machine” is a concept that the industry has run with, summarized in the original Sun Microsystems slogan, “Write once, run everywhere” (WORE). Some of the most notable virtual machines, or virtual-machine-like products and their compatible programming languages, followed Java’s machine-independent lead.
Squeak and MicroEJ improved upon the virtual machine concept by creating a situation where despite differences across machines, the code written would execute even more uniformly by better replicating bit-identical images across machines, along with their corresponding device types and operating systems. So Java, Squeak, and MicroEJ remain unique regarding their inherent universality.

Pros

  • Made history with Virtual Machine
  • Remains among few universal programming languages
  • Among the most popular languages ever
  • Popularized by Web applications

Cons

  • Criticized for lack of true universality
  • Limited to processes all machines can execute
  • Poses problems in production environments
  • Java’s abstraction essence slows execution

Price

The good news is that if you want to learn Java and mess around with the programming language to see how your programs run, you don’t have to pay anything. At least, Oracle warns, not until the end of 2020. You can download the Java Development Kit from Oracle using the link below. If you’re a company that relies on Java to function, you will be paying a regular subscription fee, and the prices get complicated beyond what we can tell you here. It used to be next to free for you to use Java, and now, for enterprise customers, it won’t be.

​Where to get it

​Ideal For

​Java is ideal for what it was designed for—running across platforms and machines with its virtual machine. It’s not so ideal for production environments because of its essential functionality which relies on an abstraction layer. This makes it more compatible but prevents it from competing with some other languages on speed.?

Features

​The machine-learning industry and its peers seem to vouch for Python as the single most trusted programming language for machine learning. It’s not unnecessarily complex, and it features a breadth of libraries, making it a winner among data scientists.

Pros

  • The readable code makes for easier maintenance
  • Basic Python code is widely universal

Cons

  • Some Python libraries are OS-specific
  • Can execute faster than some Java

Price

​To program and develop in Python, you can download the latest Python development environment using the link below.

​Where to get it

​Ideal For

​Python is ideal for machine learning applications, particularly when you don’t need compatibility across different operating systems.

Features

​R is much better than Python at creating visualizations and is often measured by data scientists to be the second-biggest programming language in the data science industry, also preferred by many academics and data wranglers.

Pros

  • Excels with statistical information
  • Many big libraries
  • Open-source

Cons

  • Fills up RAM quickly, leading to capacity problems

Price

​Because R is open source, theoretically, it’s free to program in R.

​Where to get it

​Ideal For

​R is ideal for many of the same features Python works best for, with visualizations perhaps being among its computational competitive edges. Consult your team’s programming experts to determine whether your project is more amenable to being programmed in Python or R.

Features

​Often confused with Java, it’s actually quite different from Java in that it’s been around virtually as long as Java—almost. The reason JavaScript was developed alongside Java is that its script is much simpler than that of Java. It was created for applications, often on the Web, that don’t involve the complexity of Java. You’ll also want to look into ECMAScript or ES, an attempt to standardize JavaScript.

Pros

  • Simple
  • Working toward “write once, use everywhere”
  • Long track record and well-known

Cons

  • Performance can vary across operating systems

Price

​JavaScript and the ability and rights to execute and sell programs written in JavaScript, or to use them for commercial purposes, or even on a personal Web site, is a complicated licensing issue that could fill an entire article in and of itself.

​Where to get it

​Ideal For

​JavaScript is ideal for more simplistic types of OS-independent, write-once-run-everywhere types of program uses. Like Java, it’s not currently really geared toward being a major player regarding machine learning.

Features

​More of a hard-core programming language that’s less intuitive for non-programmers than Python, C requires you to spell out a lot more than Python does. For machine learning tasks, C will have you enter lots more details that Python simply assumes. Elements of C are incorporated in C++, so this is not a competitor to C++, but rather an integral part.

Pros

  • Imperative programming
  • Can result in faster execution than Python

Cons

  • Not as much ready-to-go functionality as Python
  • Code looks messier
  • Lacks Python’s “garbage collection”
  • The code is lengthier than Python’s

Price

​You can download many IDE’s (Integrated Development Environments) for C from several Web sites, usually at no cost. The link below is just one place you can download a program that will allow you to program in C code and test it out!

​Where to get it

​Ideal For

​C is widely used in machine learning although those specializing in the machine learning aspect of their programming often want to choose Python over C because Python makes the process so much easier and automates programming tasks that are fairly universal but that need to be spelled out in greater detail in C.

Features

​C++ is still the chosen method of true computer programmers who make the nuts and bolts of the computing technology behind machine learning run. That’s why the question of C++, an advanced iteration of C, is not one of C++ versus Python. Rather, C++ technology is complementary to Python’s, in that C++ is used by the hard-core computer programmers to make sure the underlying computer functionality best complements the data scientists who take advantage of Python’s simplified language more geared toward the machine learning aspect of the operations.


That said, the machine learning aspects of the operation are still dependent upon the underlying computing power, which is best managed by C++, a variant of C.

Pros

  • Key to managing computer resources efficiently
  • A higher degree of specificity

Cons

  • Like C, it has a higher learning curve for non-programmers

Price

​Theoretically, you can program in C++ for free by downloading many development environments that support C++.

​Where to get it

​Ideal For

​C++ is the ideal language for programmers not afraid to get their hands dirty in precise code. It plays a critical supportive role in the management of machine learning processes.

Features

​Swift is comparable to Python in functionality and provides a decent means of accommodating machine learning tasks.

Pros

  • Somewhat amenable to the programming data scientist
  • Somewhat commonly used in the machine learning industry

Cons

  • Not as widely used as Python
  • Lacks the libraries of Python

Price

​Allegedly free for anyone to code in, Swift is open-source, albeit part of the tightly controlled Apple ecosystem.

​Where to get it

​Ideal For

​Swift, despite limited library availability, is a worthy contender to Python’s market dominance in the realm of best languages for machine learning.

Features

​Well known, particularly in the realm of Web and mobile apps, Ruby makes our list of best languages for machine learning because of its widespread popularity, at least historically, among Web and mobile developers. It includes lots of Web-oriented features, and it adapts quickly to change.

Pros

  • Highly adaptable
  • Web-oriented features
  • Best with quick Web development

Cons

  • Debugging isn’t always easy

Price

​It’s difficult to find a decent free IDE for Ruby. As with Java’s encroaching costs of development, the bad guy here is big corporate Oracle.

​Where to get it

​Ideal For

​Ruby is great for Web development, and not so great for machine learning.

Features

​Having started as an alternative to Java, Scala offers a robust level of customization at the computational and speed level that rivals Python in some ways. Frankly, if you’re comparing Scala versus Python, you need a team of computer programmers and data scientists to figure out which is better because it will depend upon the distinct requirements of your project.

Pros

  • Levels of precision that rival Python’s
  • Can execute processes faster than Python
  • May be best for your specific project

Cons

  • Programming can sometimes be more cumbersome
  • Differentiators are complex and hard to evaluate

Price

​Several organizations and businesses offer free IDEs for Scala.

​Where to get it

​Ideal For

​Scala may work best for some machine learning processes, however, whether it rivals Python for your specific circumstance is a question that depends upon the specific complexities of your machine learning project.

Features

​Julia is the new, trendy kid on the block in contenders for machine learning. Her creators promise she’s “fast and easy,” in other words, fast like C++ but easy like Python—the best of both worlds.

Pros

  • The potential for faster functioning in future
  • “Fast and easy”
  • May be best for your specific project

Cons

  • Processing speeds trail R’s
  • Debuggers are less advanced

Price

​As with all the other best languages for machine learning, except for those controlled by Oracle, five IDE’s are offered free for your programming pleasure.

​Where to get it

​Ideal For

​Julia is ideal for experimentation and has not yet fully matured into a full-fledged, industry-trusted language to top our list of best languages for machine learning yet.

The Verdict

If you’re more of a data scientist, consider investing your time in learning Python. While Python won’t be ideal for building all your machine learning tasks, it is likely sufficient for most data-engineering tasks. If you’re able to learn C++ besides Python, this will be a great asset to you.Java and Ruby are interesting and rather universally compatible across the Web with whatever OS is thrown at them, however, Oracle’s proprietary nature seems to follow a well-worn path to the grave which will eventually culminate in Java’s decline in popularity. Oracle is taking Java’s universality for granted, and that’s a very dangerous thing to do, however, Java might just be popular enough that Oracle will survive any losses it incurs by imposing big costs for companies that want to continue developing Java on a large scale.

The bottom line—go with C++ for your back-end machine learning tasks, but ease the load for your data scientists specializing on the learning process itself by integrating Python, which appears to be emerging as the industry leader regarding machine learning in its own right. Five stars for Python as a complementary language to C++; Oracle loses points for taking a hammer to its longstanding policy of keeping Java development mostly free for those who want to program in it.

As a quick addendum, consult your programmer who is equally knowledgeable in R, Scala, and Python to determine whether for your particular purposes Scala, R, or even potentially Julia pose any competitive edge over Python for your unique and highly specialized machine learning application. And thanks for beginning the tremendous, fun, and magical journey of determining which of the best languages for machine learning best meet your needs.

Featured Image: Image by Pete Linforth from Pixabay