With an increase in data with time, it has become paramount to be able to analyze, predict, and process that data. With the advent of Artificial Intelligence and Machine Learning, developers have successfully been able to do data analysis and predictions. Today, using Artificial Intelligence and Machine Learning, organizations and companies try to make the user experience seamless and smooth. Think about it in this way. What is the most important thing for organizations? They want to arrive at insightful results and decision making after analyzing certain data. ML and big data have been making it easy for such organizations now.

Python, on the other hand, is known to have easy syntax, east readability and clarity. It has vast arrays of libraries and user-developer reliability. In the last few years, Python has become one of the most used languages with Machine Learning. Choosing an appropriate language is really important when you decide to learn Machine Learning. Since Python is the most intuitive and efficient language, developers or data analysts must take machine learning training.

Machine Learning requires a very basic knowledge of mathematics and calculations, however, there is nothing to be scared of. If you are not very good at Maths, with practice, you can achieve whatever you want to.

It is also important to note that the Python community has created many modules over the year to make ML even more simple. People undertaking Machine Learning with Python course are on the rise considering the demand. However, one must first know all the reasons why Python is preferred over any other language.

Here we list down 6 reasons why you must take up Machine Learning with Python –

  1.  Choice of Libraries

With time, as the community of Python grew, the number of libraries available for Python users also increased. Now, we are at a stage where coders don’t have to write codes for base level items because it is highly likely that the library for that already exists. This is one of the most important features of Python as it saves time and makes work more efficient and productive. The term library can be seen as a module or group of modules designed by various sources. Coders are able to use functionalities designed by other coders. In Machine Learning, when a lot of data needs fast processing, Python libraries handle and transform data rather quickly.

  1. Support and Community

As mentioned before, Python provides access to a bigger coders’ community. Python is an open source language. With its huge popularity, coders now have more resources available to them when it comes to problem and error solving. It is not just convenient but fast too. In many cases, such a huge community helps you find solutions for most problems. Coders are always ready to help each other and bring new modules and ways on board. Once a coder or developer knows the basics of Python, with the help of a course and further, with the help of the community it is easy to do Machine Learning too.

On Python forums and community pages, a lot of documentation on Python is available which is also accessible to all.

  1. Platform Independence

There is a very strong reason why developers call Python versatile. Apart from having access to huge libraries, it is also a platform independent language. During Machine Learning, developers can run the language on many platforms such as Windows, Mac, Linux, etc. The transfer from one platform to another is also very smooth. It requires only a few changes in the source code. Developers often have to test their code on different applications. When transferring is this easy, time-saving becomes another huge attribute to prefer Python over any other language.

  1. Less Coding

Artificial intelligence requires a lot of algorithms and we know that. If a coder starts to test them all using a new code every time, it will never work out. But thankfully, Python helps in easy writing and execution. Python has a very comfortable testing process. Python has the ability to apply the same logic on even one-fifth of the code which makes it quick and efficient. What can be better for developers than to write less code and still get better performance and productivity?

  1. Flexibility

Python is robust and it’s based on Rapid Application Development (RAD). It also provides the option to choose between scripting and object-oriented programming (OOP). With so many algorithms in Machine Learning, it is easy to check the majority of code in the integrated development environment (IDE) itself. Python is a very flexible language as it works perfectly for the back end and for linking data structure. Python can be combined with other languages to reach the goal.

Source – Stack Overflow

  1. Future and Scope

StackOverflow predicts that the popularity of Python is only going to increase further. Big organizations and businesses are looking for developers who can do Machine Learning using Python. Python is a strong language in itself, and when combined with Artificial Intelligence, its possibilities are limitless. The future of the language and those who have learned to use it with ML seem very bright. Python also smoothens the learning curve, so if you are thinking to take up a course, do not wait any longer.