Here are the ten most essential programming languages for machine learning and artificial intelligence.
With the growing popularity of data science, machine learning and artificial intelligence have come an increasing demand for developers with the skills needed to fill roles focused on those fields.
One sign of that demand is the booming number of users of GitHub, which is owned by Microsoft, over the past year.
In just 12 months, the software development community and platform added more than 10 million new users, bringing its total number of developers to more than 40 million, the company said in November — nearly 80% of whom are located outside the United States. Additionally, in a year, 44 million repositories were created, and 87 million pull requests were made.
But what are those GitHub users learning and using? As we head into 2020 when the growth in these fields seems likely to continue, here are the ten most important — whether due to popularity or to potential — programming languages for data science, machine learning and artificial intelligence.
Python is another popular language on Github, but that may change soon. Thanks to its popularity among web developers and data scientists, the programming language, which started as a fun project, is rapidly gaining users and may soon become the most popular in the world.
This open source and graphics-based language and software environment, used for statistical computing and graphics, is in demand for both data science and machine learning. Companies like Facebook and Google use R for machine learning tasks such as setting up a decision tree.
Although SAS is not open source, R can thus be used for data analysis and is in fact one of the oldest languages designed for this purpose. A reliable platform with a stable platform, SAS has a wide range of libraries despite being a closed source.
Although Java was left behind by Java to become the second most used language on Github, that doesn’t mean it doesn’t matter anymore. This common use language is used for backend systems and applications, and helps to make portability between systems possible.
TensorFlow is ideal for numerical calculations and large-scale data. This open source software library supports distributed computing, making it an alternative to training neural networks. It powers Google’s large-scale applications.