The increasing demand in artificial intelligence and machine learning specialists has put a spotlight on the skills and knowledge needed to excel in this profession. In particular, many aspiring machine learning engineers want to know which programming languages they should master to be competitive in this career.
While there really is no “best programming language” for machine learning, there are certainly some that are more appropriate for machine learning tasks than others. The purpose of this blog is to discuss how machine learning engineers choose languages to work with, and to profile a few popular programming languages for machine learning.
How do you choose a machine learning language?
You can start by looking at the languages current machine learning engineers use. Because this profession is relatively new, language choice is often based on factors like the machine learning task itself and the industry and/or academic background of the engineer.
The best language for the task
According to a 2017 survey of machine learning developers and data scientists by Developer Economics, many developers select a language based on the type of project that they’re working on. For example, many of those surveyed said they preferred using either R or Python for sentiment analysis tasks. Python is also popular for natural language processing (NLP). Those using machine learning for security and threat detection were more inclined to use C/C++ or Java.
Although not mentioned in this survey, Scala is the primary language used on the Apache Spark platform. Data engineers and machine learning engineers who are working with Big Data are often proficient in Scala.
How background plays a role
Machine learning engineers often bring their academic and previous industry experience into their new roles that they move into. An example of this is R. The R language was created by statisticians in academia for data analysis and modeling. R has been widely adopted in industries like bioinformatics and bioengineering largely because of the academic backgrounds of the engineers and data scientists in those roles.
As another example, Python is one of the more-commonly taught introductory programming languages in university and online courses. Individuals jumping straight into a career in data science or machine learning will find support with the language’s many machine learning libraries.
Individuals with a background in Java-based enterprise application development often continue to use this language in machine learning roles. As mentioned previously, Java is the language preferred for creating enterprise applications for network security and anomaly detection.
What are the most widely used machine learning languages?
We’ve already briefly mentioned a few machine learning programming languages, but the question of which are the most widely used is somewhat subject to debate. A 2018 GitHub analysis does provide some insight, however. The site’s survey of all public and some private repositories tagged as “machine-learning,” found the following languages to be in the top 10:
We’ll note that because this was an analysis of mostly public repositories, this survey may not give the best insight into which languages are being used in enterprises or large companies. It does, however, provide a good picture into the languages favored by individual developers.
We’re not going to go into detail about all 10 languages on the GitHub list, but we wanted to profile a few that are particularly significant to machine learning and the larger data science community.
Python’s place at the top of the GitHub list is likely due to its use as an introductory programming language and its large number of machine learning-related libraries including:
- Scikit-learn – Includes algorithms for clustering, classification, and regression tasks.
- Matplotlib – Plotting and visualization tools
- Pandas – Data wrangling, manipulation, and analysis
- TensorFlow – Includes machine learning applications like neural networks (TensorFlow is available as a library for other languages as well)
In addition, compared to other programming languages, Python has simple and easy-to-learn syntax. This makes it ideal for quick development and rapid prototyping.
A critical part of a machine learning engineer’s job is understanding statistical principles enough to apply them to big data. It’s no surprise then that a programming language designed by and for statisticians would play a role in machine learning.
Popular machine learning and data science packages in R include:
- tidyr – Cleans and organizes data into rows and columns
- dplyr – Data wrangling and management
- ggplot2 – Data visualization
- randomForest – Implements random forest classification/regression algorithms
- e1071 – Contains functions for clustering and classifier algorithms
Scala has fewer machine learning libraries than Python or R, but is becoming more widely used by data scientists and machine learning engineers because of its relationship with Apache Spark. Spark is an engine for large-scale data processing written in Scala and provides a native Scala API. Knowledge of Scala is an essential skill not only for machine learning and data science, but individuals interested in data engineering as well.
While not unique to Scala, MLib, Spark’s machine learning library, contains the most frequently used machine learning algorithms and workloads including classification, regression, clustering, and model evaluation.
When it comes to the best machine learning language to use, your decision ultimately comes down to your background and the type of work you plan to do. Fortunately, machine learning can be applied to multiple programming languages, so you’ll probably find a fit even if you aren’t familiar with the most common ones.
Algorithmia is language-flexible
Algorithmia makes deploying machine learning models easy no matter the programming language they’re built in. So whether your team prefers one ML language or works with models in several different languages, the path to production of those models is fast and frictionless on the Algorithmia platform.