Why Python for Machine Learning?

Published in

Women Who Code Delhi

5 min readMay 25, 2020

Python is currently the most popular programming language for research and development in Machine Learning. According to Google Trends, the interest in Python for Machine Learning has spiked to an all-new high with other ML languages such as R, Java, Scala, Julia, etc. lagging far behind.

If Python is so much popular, then one must think Why Python? What does Python provide that other languages do not? What additional tools are present in Python that are not present in other development languages? Well let’s see and clear out doubts.

Reasons Why Python is chosen as the best programming language for Machine Learning?

To implement your ML and AI aspirations, you should use a programming language that is stable, flexible, and has tools available. Python offers all of this, which is why we see lots of Python AI projects today.

So let’s see the qualities of Python 🐍

Python is Simple to Understand and is Consistent 😃

Nobody likes excessively complicated things to learn and apply and so the ease of using Python is one of the main reasons why it is so popular for Machine Learning. It is simple with an easily readable syntax and that makes it well-loved by both seasoned developers and experimental students.

While complex algorithms and versatile workflows stand behind machine learning and AI, Python’s simplicity allows developers to write reliable systems. Developers get to put all their effort into solving an ML problem instead of focusing on the technical nuances of the language.

In addition to this, Python is also supremely efficient. It allows developers to complete more work using fewer lines of code. Many programmers point out the frameworks, libraries, and extensions that simplify the implementation of different functionalities. It’s generally accepted that Python is suitable for collaborative implementation when multiple developers are involved.

Since Python is a general-purpose language, it can do a set of complex machine learning tasks and enable you to build prototypes quickly that allows you to test your product for machine learning purposes.

You can see, it’s just one feature we are talking about for Python and this only feature is making us love Python more!

Python has multiple Libraries and Frameworks

Implementing AI and ML algorithms can be tricky and requires a lot of time. It’s vital to have a well-structured and well-tested environment to enable developers to come up with the best coding solutions.

The ML algorithms are very complex, but Python is the rescue with an extensive range of libraries and frameworks. A software library is a pre-written code that is used by developers during a common programming task. These libraries help the programmer and reduce development time. Python has a rich technology stack and has a different set of libraries for Machine learning.

Keras, TensorFlow, and Scikit-learn for machine learning
SciPy for advanced computing
Seaborn for data visualization
Pandas for general-purpose data analysis
NumPy for scientific computing and data analysis

Scikit-learn features various classification, regression, and clustering algorithms, including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to work with the Python numerical and scientific libraries NumPy and SciPy.

With these solutions, you can develop your product faster. Also, the developers can use the existing libraries to implement necessary features.

Python is Flexible 😌

Python is known as the most flexible language in machine learning. It provides various options for users. The flexibility factor reduces the possibility of errors. It let the programmers take the situation completely under control, and work on it comfortably.

Programmers can combine Python with other languages to get the desired results.
You do not require recompiling source code. Programmers can implement their changes in coding and see the results quickly.

Python is Platform Independent

Platform independence refers to a programming language or framework allowing developers to implement things on one machine and use them on another machine without any (or with only minimal) changes. One key to Python’s popularity is that it’s a platform-independent language. Python is supported by many platforms including Linux, Windows, and macOS. Python code can be used to create standalone executable programs for most common operating systems, which means that Python software can be easily distributed and used on those operating systems without a Python interpreter.

What’s more, developers usually use services such as Google or Amazon for their computing needs. However, you can often find companies and data scientists who use their machines with powerful Graphics Processing Units (GPUs) to train their ML models. And the fact that Python is platform-independent makes this training a lot cheaper and easier.

Python has Great Community and Corporate Support

Python has been around since 1990 and that is ample time to create a supportive community. Because of this support and the simplicity of this language, Python learners can easily improve their Machine Learning knowledge and the implementation, which only leads to increasing popularity.

In the Developer Survey 2018 by Stack Overflow, Python was among the top 10 most popular programming languages, which ultimately means that you can find and create a development company with the necessary skill set to build your AI-based project. If you look closely at the image below, you’ll see that Python is the language that people Google more than any other.

Also, Corporate support is a very important part of the success of Python for ML. Many top companies such as Google, Facebook, Instagram, Netflix, Quora, etc use Python for their products. Google is single-handedly responsible for creating many of the Python libraries for Machine Learning such as Keras, TensorFlow, etc.

It’s a well-known fact that the Python AI community has grown across the globe. There are Python forums and an active exchange of experience related to machine learning solutions. For any task you may have, the chance is pretty high that someone else out there has dealt with the same problem. You can find advice and guidance from developers. You won’t be alone and are sure to find the best solution to your specific needs if you turn to the Python community.

So these are some of the reasons we choose Python for Machine Learning. I hope you find this blog informative. I would greatly appreciate it if you kindly give me some feedback.

Also, these are some of the links I referred to while writing this blog:

Thank you for reading and Happy Learning! 😀