Machine learning is the major caretaker of innovation for over a decade now. This language is categorized into various sections such as unsupervised, supervised, reinforced, and semi-supervised learning.
Unsupervised learning identifies various patterns in input data that is unlabeled. The supervised learning identifies relationships in labeled data. The reinforced learning stimulates programs to elaborate and repeat processes to achieve the desired outcome. And the semi-supervised learning is a combination of unsupervised and supervised learning.
While several machine learning products are developed for these categories, languages used may differ. We have explained the top 5 programming languages for machine learning below. Although there are many more, these 5 stand out due to high flexibility, feasibility, and scalability.
Python
Now, this goes without saying. Python has been linked to machine learning for years. Whenever we think of ML programming, Python automatically comes as the first choice.
It is widely utilized for various ML applications, in big data, and other artificial intelligence products. This is due to the fact that Python has pre-built libraries for ML. Further, Python is adaptable to any OS and it is open source, which makes it easily accessible to everyone.
R
After Python, R is another programming language greatly accepted by the ML community. You can use R for data analysis, data visualization, ML tasks like classification, regression, and decision tree.
Fortunately, R is also open source, which makes it not only easily available but also easily installable. It integrates with other languages without any issue.
C++
If you are even remotely related to computer science, you would know that C++ is an old yet frequently used language even today. For machine learning, in particular, C++ is best for resource-intensive apps. And as this is a statically typed language, its execution is comparatively faster than other languages.
JavaScript
JavaScript is a dynamically typed, high-level language that offers flexibility to machine learning projects. One great advantage of JavaScript in machine learning is that it provides an entry point to front-end and web developers, which is otherwise difficult.
Java
Java offers the ability to implement various algorithms. This language is very secure due to its sandboxes and bytecode. Overall, Java is an enhanced version of C++ which removes the complexity and security issues of C++.
Conclusion
Of all the languages that we use for ML, Python steals the show. It is the best and most utilized language. But, other languages are utilized as and when required for specific and relevant applications. If you are trying to step foot in this field, you should know at least one of these languages thoroughly. If you know more than one, that is always a plus point. We suggest starting with Python.
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