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Noria Sinta
Noria Sinta

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4 Reasons Why Python Is Best!

Machine Learning is the latest trend these days. Based on Forbes, Machine studying patents climbed at a 34 percent speed between 2013 and 2017 and this is only set to rise later on. So much so that Python is your very best programming language for Machine Learning according to Github. However, while it is clear that Python is the most popular, this article focuses on the all-important question of "Why is Python the Best-Suited Programming Language for Machine Learning?" Python is now the most popular programming language for development and research in Machine Learning. But you don't need to take my word for it! According to Google Trends, the interest in Python for Machine Learning has spiked into an all-new high along with other ML languages like R, Java, Scala, Julia, etc., far behind.

1. Python Is Not Difficult to Use

Nobody enjoys excessively complicated things and so the simplicity of using Python is one of the main reasons why it's popular for Machine Learning. It is simply having an easily readable syntax and that makes it well-loved by both seasoned programmers and experimental students as told by Digital Marketing Agency Pakistan). The simplicity of Python means that developers can concentrate on actually solving the Machine Learning difficulty rather than spend all of their time (and energy! such as Islamic information) Understanding only the technical nuances of the speech. In addition to this, Python is also highly effective. It allows programmers to finish more work with fewer lines of code. The Python code can be easily understandable by people, making it ideal for making Machine Learning models. With all these benefits, what's not to adore?!!

2. Python has numerous Libraries and Frameworks

Python is already very popular and consequently, it's hundreds of unique libraries and frameworks that may be used by developers. These libraries and frameworks are really beneficial in saving time which subsequently makes Python even more popular (That's a valuable cycle!!!). There are many Python libraries that are especially helpful for Artificial Intelligence and Machine Learning. Keras is an open-source library that's particularly focused on experimentation with profound neural networks. (They seem to be rather popular! Even more than VPN discounts) Also, Scikit-learn can be utilized in conjugation with NumPy and SciPy.

3. Python has Corporate and Public Support

Python has existed since 1990 and that is ample time to create a supportive community. As a result of this support, Python students can easily boost their Machine Learning knowledge, which only leads to increasing popularity. And that is not all! There are many resources available online to promote ML in Python, ranging from GeeksforGeeks Machine Learning tutorials to YouTube tutorials that are a significant help for learners. Also, Corporate assistance is an essential part of the achievement of Python for ML. In reality, Google is single-handedly responsible for creating many of the Python libraries such as Machine Learning, for example, Keras, TensorFlow, etc..

4. This is an important reason why Python is so well known in Machine Learning.

A good deal of cross-language operations can be performed easily on Python due to its portable and adaptive nature. There are many data scientists who favor using Graphics Processing Units (GPUs) for training their ML models on their own machines and the portable nature of Python is well suited to this. In addition to this, Python is also integrated using Java, .NET parts or C/C++ libraries due to its nature.

Top comments (1)

shaijut profile image
Shaiju T

😄, I used to have questions like which language is better ? After discussing with my friends and listening to people online, I realized that Its better to stop asking these questions. Instead start asking which tool is better for current Job and Trend.

Choose the right tool for the Job.

  • Based on your experience, you can use C# or Java for building enterprise and large applications.
  • Instead JSF you can think of using Anuglar orReactorVueetc. for Front End.
  • Python for Machine Learning.
  • Go for Micro Services based performant applications.

In future maybe today's Languages and Framework may be outdated, so to survive you will be forced to learn new language of that time.


  • Developer Happiness, stick to the language which make your life easier, like easy to read syntax, maintainable, has Good IDE. I like C# for current work, and its up-to you to decide what you like.

  • Its always good to be Open to learn any language as required and Choose the right tool for the Job.

Hope this helps.