Reasons Why Python is Preferred in Machine Learning A Brief Overview

Reasons Why Python is Preferred in Machine Learning A Brief Overview
Table of Contents

When it comes to the important coding languages, Python emerges as the most popular language across the globe. Especially for machine learning, Python proves to be the highly preferred option. The simplicity, flexibility, consistency, and platform independence are some of the prominent factors that make Python suitable for machine learning projects.

Some of the top reasons for the increasing preference for Python in machine learning are listed below.

1. Easy to Learn and Understand

Recognizing the data patterns is the main role of machine learning. While machine learning engineers have a lot to handle, starting from data extraction to intelligent algorithm development, Python can help in quick and easy data validation. Being easy to learn as well as understand, Python makes it easier for the engineers to work on the machine learning projects. With quick implementation, Python enables the machine learning engineers to promptly validate their ideas.

2. Shortens the Codebase

Writing programs using machine learning involves the implementation of various complex algorithms as well as scientific calculations. While this can increase the program lengths, Python helps in making it simple. With Python, you can easily shorten the lengthy codebase by nearly 1/5 times. The compact line of code provided by Python can help in enhancing the overall results. With the simplicity of Python, the developer can focus on solving the ML problem, rather than the language nuances.

3. Platform Independence

The platform independence feature of Python is another important reason for its growing preference in machine learning. The platform independence enables the developers to implement things on different machines with minimal or no change. Python is supported by various platforms like Windows, Linux, and macOS.

Using the python codes, developers can easily create standalone executable programs for different operating systems. Moreover, there are companies that use machines with powerful GPUs to train machine learning models. With the independent platform of Python web development services, this training becomes a lot easier as well as cheaper.

Also Read: Node.js vs. Django: Which is Better For Web Development?

4. Flexibility and Versatility

Python offers great flexibility as well as versatility to the developers. Python can be readily used by any other programming language in order to achieve the goals of a business. As Python makes instant change possible, there is no need for recompilation of the source code, thereby enabling faster results.

Moreover, the flexibility of the coding language helps in minimizing the chances of bug occurrence. As Python for ML can run on all the platforms like Unix, Windows, macOS, Linux, and more, running tests on any platform becomes easy. Hence, using Python as the coding language for machine learning can help in saving a lot of time as well as money.

5. Extensive Libraries and Frameworks

One of the important reasons why Python is preferred for machine learning is the easy access to the great library. As implementing ML can be time-consuming as well as tricky, the extensive library makes the task easier for the developers. Performing complex tasks without rewriting the codes becomes possible for the developers with the extensive library of Python.

In order to reduce the development time, the programmers can rely on python libraries as well as frameworks. Moreover, as machine learning relies on probability, statistics, and mathematical optimizations, the extensive library of Python can help in making the studies easier for the data scientists. It also enables faster development for the companies. Some of the libraries of Python are Keras, Pandas, StatsModels, Seaborn, Matplotlib, and more.

6. Low Entry Barrier

Lowe entry barrier is yet another significant reason that makes Python a preferable option for machine learning. As the language of Python is simple and easy to learn, it has a low entry barrier. The data scientists can easily master the programming language in much less time and contribute towards the success of the machine learning projects. Python is as easy as learning the English language. The simplicity of the phrase structure of Python enables the developers to easily work with different complex systems.

Also Read: Reasons Why Python is Popular in Data Science and Data Analysis

7. Huge Community

Python has a huge community of supporters. Whenever you face any difficulty or problems, you can easily avail of quality assistance from the experienced members of the community. The thousands of expert coders in the community help in providing valuable inputs to the users and helps in making their tasks easier. With a wide pool of resources, the pros, as well as the beginners, can be benefited equally.

8. Growing Popularity

The popularity of Python as a programming language is increasing day by day. The increasing demand of the language around the globe makes it an ideal choice for machine learning projects. The simplicity and ease of maintaining Python are one of the major reasons for the growing popularity.

Owing to the reasons stated above, Python proves to be the best option for machine learning. Along with quick data validation, it can also ensure error-free machine learning projects. With Python, the maximum success of the machine learning projects can be ensured.

Mobio Solutions is a leading Python development company. With the ability to build custom applications Python becomes one of the most popular programming languages among various industries. At Mobio Solutions, our machine learning and data analysis & mining services deliver high-quality applications with enhanced features to increase the ROI of the business.

Share it:
Mobio Solutions is a leading Business Technology Solutions Company. We have been providing cutting-edge business technology solutions for SMEs and large enterprises worldwide. We specialize in creating innovative technology solutions that solve complex real-world business challenges.