The Best Programming Languages for Machine Learning

The demand for Artificial Intelligence (AI) and Machine Learning (ML) applications has been steadily growing throughout the years. To paint a better picture, the International Data Corporation (IDC) reports that global expenditure on AI is set to hit $98 billion by 2023, making it one of the fastest-growing software purchases to date. ML is not too far behind, as a similar report from IDC shows how ML patents grew at a 34% annual growth rate, too.

The technological revolution brought about by AI and ML has drastically changed how companies operate. A discussion on HP Tech Takes notes how AI is becoming one of the most critical tools for small businesses and large corporations alike—thanks to its automation and cost-saving abilities. On top of this, AI is also streamlining operations and expanding customer reach, more than any human could ever do.

It’s hard to deny that AI/ML development is singlehandedly upending industry standards. In fact, AI and ML-related jobs are among the most in-demand right now. So, if you’re looking to get in the field, it’s important to understand that AI/ML development is always looking for fresh talent.

To kick-start your career, it helps to know which languages are fit for the job. While any language can be used to develop AL/ML applications, here are the best of them and why:

Python

With Python’s varied library ecosystem and flexibility, most programmers turn to Python for ML. The Code Repository’s analysis of pull requests ranked both Python’s Numpy and Scipy as the most-used libraries for ML. The readability and low-entry barrier of the language make it easier to work with when dealing with complex systems and clearing relations between elements.

C++

While most ML projects are published in Python, C++ comes in second. This is mainly because C++ has numerous advantages over Python. For one, it’s statically typed, which means no type errors will be seen in runtime. Another is the sheer power of C++ in creating more compact code with faster runtime. Often the issue with ML development is speed, and executing it in C++ compiled code is faster than the other languages in this list. The most popular frameworks for ML in C++ are Google’s Tensorflow, Microsoft’s LightGBM, and Apple’s TuriCreate.

JavaScript

The king of the web comes in at third. With a huge user base and the use of Node.js, JS now has extensive frameworks and libraries for different applications of AI and ML. Its vast and still growing npm ecosystem alone makes it a great choice for developing ML. Tensorflow.js is a versatile library for developing ML models in Javascript (see a demo). Other libraries include brain.js and Synaptic for building neural networks. Natural is becoming a popular framework for natural language processing—a branch of ML used in speech recognition. Moreover, developing ML projects in JS also means they’re accessible to web and front-end developers.

Conclusion

Of course, the language to use for ML and AI programs all boils down to preference. Here are other languages you can use:

• Java
• C#
• Julia
• Shell
• R
• TypeScript
• Scala

But there’s a reason why the ones we mentioned are the most used. Think about the kind of ML and AI project you’d like to do. For instance, some of them require stochastic approaches where you need to do the same experiments over and over, or deal with matrices—for which C++ is the best candidate.

On the other hand, the rise of GPU-accelerated computing has made Python—together with CUDA Python and cuDNN—capable of parallelism. Plus, the sheer versatility of Python alone makes it the best language for handling data and data structures.

If you’re planning to run your project cross-platform, JS would be a great choice. Similarly, you can use frameworks Django and Flask to run Python on web browsers.

As you can see, these options offer different advantages, so it may be worth trying each one to experience them for yourself too.