When it comes to machine learning applications, operational and training phases for algorithms are quite different. Most of the companies prefer different languages for different phases. The overall idea behind this objective is to do some experiment in the development phase and figure out language that can be best suited.
Machine learning was rated as one of the highly admired skills in the last couple of years. Today, more than 80% of the developers focus on acquiring this skill and they mostly prefer Python programming language for this purpose. The research also demonstrated that over 57% of the data scientists consider Python and other languages are not even near to it in terms of popularity. When it comes to user following, it has even beat C# and shown tremendous growth in several machine learning applications. It has won hearts of developers that belong to all the ages and abilities.
Why Python?
There are several common and uncommon reasons behind choosing Python programming language over other languages and you need to get into every single detail to understand it. Here we have listed all the important reasons that persuade data scientists to prefer Python programming language for machine learning:
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1. The Simplicity:
2. The Flexibility:
As Larry Dignan, the Editor-in-Chief of ZDNet, summarized in one of his interviews,
“Python is recognized as a general-purpose programming language which can easily be suited to a wide range of machine learning activities. It is widely popular among data analysts as it can effortlessly compute data from NoSQL and some other advanced databases.”
3. Backgrounds & Available Libraries:
Professional background is another key factor while prioritizing the programming language. Python has become a vital part of data visualization and professionals who are studying data science as a part of their university training usually prefer Python over others. In recent time, it has also evolved into different native languages that data scientists can easily understand.
4. Availability of a vast AI-focused Libraries:
5. Ease of Use & Compatibility:
The ability of Python to employ modular components makes it highly compatible with other programming languages. It is tremendously powerful and adaptable with a wide range of technologies. It has also got amazing component integrity and you can easily import other languages to use them with Python.
6. Human Resource:
As Piotr Majchrzak, the Co-CEO at Boldare, explained the mindset of the programmers in one of his conferences,
“Nowadays, programmers are extremely cautious about their choices and they do a lot of research before choosing a specific programming language. For example, Java is mostly selected when it comes to developing algorithms for fraud detection and network security. However, Python is highly preferred language when it comes to sentimental analysis and Natural Language Processing (NLP).”
7. Productivity:
Data scientists who are coming from diverse backgrounds find Python much convenient. Its inherent ease and readability make it easy to understand relative to other programming languages. Besides this, availability of multiple analytical libraries means data scientists in each sector easily get packages which are tailored to their specific needs and they can be quickly downloaded free of charge.
8. Multi-Paradigm:
As Python is designed in such a way to have a lightweight core, the dedicated libraries can be built in it with specialized tools for different kinds of programming tasks. The ease in usability also allows data scientists to quickly go to the center of the problem without any need for supervising complicated functions within the libraries.
9. Open Source & Cost Effectiveness:
Offering an open source platform is another valid reason behind the rising popularity of Python amongst data scientists. Having such a large base means there are millions of individuals who are keen to provide you with crucial advice when you face any issues related to this language. There is always a chance that someone has already stuck with similar kind of problem and he is happy to share his experience so you don’t need to waste your time doing trial and errors. This platform is highly beneficial for newcomers who really can’t waste with their resources. They can figure out solutions quite efficiently.
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10. Python Community:
The Bottom Line:
All in all, Python has a tremendous potential to offer a lot to emerging machine learning programmers who are still in learning phase. A recently conducted survey has revealed that Python has seen a drastic increase in usage for the last few years. Experts have also alleged that Python will remain the most sought-after programming language in the next few years as well. Courtesy to the wide range of advantages of Python, in the coming time programmers may find it adaptable to be used in the data analytics field. Though it is not completely suited for statistical analysis, companies who have done a chunk of investment may try to experiment and use it for such an important purpose.
However, popularity should not be the only parameter while selecting a specific programming language for machine learning. There is no such thing called ‘ideal language for machine learning’. It all comes down to your background, why you get into the machine learning and what you want to build.