Machine learning (ML) and Artificial Intelligence (AI) are the new blacks of the technology industry. According to Forbes, Machine learning patents grew at a rate of 36% between 2013 to 2019, and this is only set to increase in the future. The world is looking forward to better personalization, best search functionality, smarter recommendations and that are what Machine Learning has brought — improving customer experience and creating values across industries.
To implement these ML aspirations, developers needed a language that is flexible, stable, and provides useful tools. Python offers this which is why it is loved by most of the developers.
Python has enjoyed a rapid rise in popularity in recent years, jostling for the position of the top programming language in the future. Stackoverflow survey 2020 results showed that Python is the top language to be used for Machine Learning.
Why Is Python The Best Suited Language For Machine Learning?
Python is currently the most used language for research and development in Machine Learning and data science. Below are some reasons why python is the best-suited programming language for ML. Let’s dive in.
1- Extensive Library Ecosystem
The strength of Python language lies in its extensive machine learning libraries available for different types of tasks. A library is a module or a combination of modules published by different platforms such as Pypi that includes an already written piece of code to enable users to reach some functionality.
Python library provides basic level coding so developers don’t need to code in the beginning. Moreover, the Python library ecosystem allows developers to access, transform, and handle data. Developers can work more efficiently and become more proficient in various python libraries so they have to spend less time on basic coding and pay more attention to the tasks that are more important in their line of work. Some of the widespread Python libraries are Scikit-learn, Pandas, Keras, TensorFlow, Matplotlib, NLTK, etc.
2- Python- A Simple & Consistent Language
Python provides concise and easily readable codes that make it loved by both experimental students and seasoned developers. As difficult and complex algorithms stand behind ML, Python simplicity enables developers to write reliable codes and pull off their efforts into solving Machine Learning problems instead of wasting time on the technical nuances of the language.
Moreover, python is easily understandable by humans which makes the top choice to build ML models. With its simplicity, it allows developers to complete big tasks in less time using fewer lines of code.
It also enables you to build prototypes and let you test your machine learning products quickly. It has a simple syntax which means that it offers faster development processes than other languages, enabling developers to test the ML algorithms quickly without having to implement them first.
3- Quick Coding From Scratch
Python offers a number of machine learning tools that enable developers to do the coding from scratch in less time. No doubt that programming done by hand shows a developer’s efficiency, yet it is a hectic and time-consuming process.
Python gives developers access to pre-written ML algorithms and frameworks that work for any type of project. Compared to all other languages, python enables the developers to directly use the tools instead of building them first.
4- Large Community & Corporate Support
As it provides a number of useful tools, and open-source libraries, python has developed a strong community base.
This language has been around since 1990 and by now, it had a great time to develop a strong supportive community. Because of the huge support, Python learners can easily get knowledge about machine learning. Businesses will really benefit from it in the future as it helps developers to master Machine Learning and implement ML solutions without any hitch.
5- Extensive & Portable
This is another reason why Python is best for Machine Learning. Due to its portable and extensive nature, it allows a number of cross-language operations.
There are a number of data scientists who prefer using GPUs ( Graphics Processing Units) in order to train machine learning models and the portable nature of Python is best suited for this.
Moreover, the portability of Python is the reason why various other platforms such as Windows, Linux, Solaris, Macintosh, etc support Python. Furthermore, python also integrates with C/C++ Libraries, .NET and JAVA due to its extensibility.
6- Independent Platform
An Independent platform means a framework or programming language enabling developers to implement things on one machine and use it on another without any problem or changes.
Python is an independent language and can be used to create standalone programs for all types of software developmentsystems. That’s not all! Developers often use services such as Amazon or Google to fulfill their computing needs. However, you will find data scientists and companies who prefer their own machines with GPUs for the training of Machine Learning models. And as Python is an independent platform, it makes training a lot easier and cheaper.
7- Quite Low Entry Barrier
Working in the Machine Learning industry means dealing with bulk data that you need to process quickly and in the most convenient manner. Due to the low entry barrier of this language, data scientists can easily pick and understand Python and start using it for machine learning development.
Python coding language is similar to everyday English language which makes it the top choice for beginners. With its simple syntax, you can work with difficult and complex operations, ensuring smoothness between the system elements.
Python is emerging as the most popular language for machine learning applications. It is very simple, highly readable, and incredibly functional which makes it the best choice for developers.
Machine Learning relies upon complicated algorithms and frameworks and Python libraries contain these complex algorithms as ready-to-use functions. Due to the reduction of time and effort, python is becoming the choice of data scientists and developers.
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