Why Python is the go-to language for machine learning

In the domain of data science, machine learning, and AI (artificial intelligence), Python has quickly emerged as the go-to language. Tracing back the origin of Python will bring you to Guido van Rossum, who released it in 1991 as his side project. Probably, he never expected, even in his wildest dreams, of it becoming the fastest growing computer language of the world in the near future. But that’s exactly what happened. Over the last few years, this programming language has experienced a steady rise to fame and is now vying to hold the top position of one of the most popular global programming languages. But it’s not just for applications ranging from scripting and web development to process automation, etc. In data science, machine learning (ML), and deep learning projects, Python has become the top choice among developers. If you are wondering what makes this programming language so special, particularly in the domain of machine learning, let’s examine the reasons.

Easier learning curve

Python is extremely simple to learn and use. It’s known for its readable, concise, and simple code. In fact, Python’s syntax is often called “math-like” (as it has a specific connection to several common mathematical ideas) and “elegant.” Thanks to Python’s simple syntax, it works faster in development than many other programming languages, thus facilitating the developer to test algorithms speedily without having to implement them. Usually, machine learning projects heavily depend on multi-stage workflows and extremely complex algorithms.  Since ML often involves collaborative coding where easily readable code is important, Python can be of help. Additionally, ML projects – especially the ones involving a lot of third-party components or custom business logic, often change hands between development teams. Using Python would mean the developers have to worry less about the intricacies of coding as they would no longer need to spend a lot of time to debug the code for syntax errors. Instead, they can spend more time on their heuristics and algorithms related to machine learning, which would help them achieve the projects’ goals fairly easily as they can stay better focused on finding solutions to problems.

Array of frameworks and libraries

Python has an array of tools, code stacks, packages, and collection of different open source repositories (that are continuously developed by people to help improve the existing methods). Thus, for a wide range of machine learning projects (that may need you to work in text, visualize the data clearly, or solve ML problems), you will always have help readily available when you are using Python. To give you an idea of how convenient this can be when you work in the domain of machine learning, here’s a brief overview:

• OpenCV, NumPy, Scikit – for working with images
• Librosa – when you need to work in audio
• Scikit, NumPy, NLTK – for working in text
• Scikit, Pandas – for solving machine learning problems
• Scikit, matpotlib, Seaborn – for visualizing the data with clarity
• SciPy – for scientific computing

With a basic knowledge of Python, you can use the above as they come with almost a zero learning curve. Thanks to Python’s highly-specialized libraries, you can even use the programming language to easily and quickly create highly-performing algorithms, which may give you a competitive edge in the landscape of new ML-centric apps.

Support available in abundance

Since Python is an open-source language, you will get an abundance of support while using it. This programming language is not only supported by a wide range of resources and high-quality documentation, but also has an active community of developers (which is pretty large as well). Thus, whenever you need any assistance or advice, you will get it all from this professional and extremely helpful community, whose members are always willing to lend a helping hand. This can prove to be extremely helpful when you have to navigate the complex world of machine learning.

Final words                                 

You may come across people who consider Python a “toy language” and say they prefer other languages to meet their requirements for “hard-coding.” However, many view Python as a fully functional substitute for dealing with the complex (and sometimes, even cryptic) syntax of a few other languages. Perhaps that’s why Python finds such a huge favor among professionals working in the domain of machine learning in comparison to other languages like C, Java, Perl, or Ruby on Rails.

Be it Python’s simple syntax and readability that support speedy testing of complex algorithms, and make the programming language accessible to non-programmers (say, for those coming from the Mathematics or Statistics field), or its machine learning-specific frameworks and libraries that reduce the time for development by simplifying the process, the advantages of using it for ML are many. Though you can use other programming languages too in your machine learning projects, there is no denying the fact that Python has become the go-to choice for a majority, which is why it should be given significant consideration.