Why Python Is Ideal Programming Language?

Why Python Is Ideal Programming Language?

Why Python Is Ideal Programming Language?

Python is a high-level, easy-to-learn, general-purpose, and object-oriented programming language. It was released by Guido Van Rossum in 1989. Eventually, Python is ruling the mobile app development market.

Due to its attractive attributes, it has become the most preferred programming language to use. In this article, we will discuss the Python programming language in detail and tell you why it is best For Machine Learning & Artificial Intelligence.

Flexibility

-Machine Learning & Artificial Intelligence algorithms are distinct from traditional software projects. As they require different skill sets, technology stack, and deep research. Python offers a flexible & stable programming language which makes the project more reliable.

-It allows developers to select OOPs (Object-oriented programming) or scripting as per their needs. Any changes can be implemented easily and the following results can be quickly accessed.

-Python is open to be combined with other languages too, to attain the desired goal.

-The chances of errors also reduce. The developer gets an opportunity to work in a comfortable environment in their preferred language.

Multiple Libraries & Framework

Python facilitates a pre-written code library that can be used by developers to solve their common programming tasks. These frameworks and libraries are really helpful in saving time, thus makes this programming language all the more popular.

There are hundreds of frameworks useful for Artificial Intelligence & Machine Learning, few set of extensive libraries are listed below:

TensorFlow is a free software library for ML apps like neural networks.

Scikit for handling common ML algorithms.

Pandas aids data analysis for general purposes.

Keras is mainly for deep neural network experimentation.

Scikit-learn is for various regression, classification, and clustering algorithms related to ML. Also, it can be used in conjugation with NumPy and SciPy.

SciPy for advanced computing.

State models for data exploration and statistical algorithms.

Scikit-image for image processing.

Matplotlib & Seaborn for creating charts, 2D plots, and other visualization forms.

Community & Corporate Support

Since Python was launched in 1990, it had ample time to form a supportive community. A developer can find a lot of Python documentation in Python forums, both online and offline. Here, ML developers and programmers can discuss their errors and hurdles. This way data scientists across the globe can help each other in solving problems.

Huge Corporate Support has also led to the success of Python. Top Multinational companies such as Facebook, Google, Quora, Netflix, Instagram, etc use Python for their products. Moreover, Google is solely responsible for generating many Python libraries like TensorFlow, Keras, etc.

Easy To Understand

Even being a high-level programming language, Python reads like English. Thus, a lot of syntax-learning stress goes off for coding beginners. Python was created to be fun to use and easy to understand.

One is able to build tools and prototypes quickly with Python, making it a satisfying experience. This is the reason, it has substituted Java as the most popular introductory language at Top Universities in the U.S.

In addition to this, this language is also supremely efficient. It allows developers to finish more work with fewer lines of code. Thereby, saves a lot of time and effort too.

Versatile

Since Python is around for so long, it has a solution package for every purpose.

For instance,

  • NumPy to crunch vectors, numbers, and matrices.
  • SciPy to do calculations for engineering and tech.
  • Pandas for data analysis and manipulation, and so on.

Thus, for any computational task to be managed, there is a Python package out there.

Also, Python can run on any platform including Unix, Linux, Windows, macOS, Solaris, Intosh, and many more. Only a few modifications in codes and small-scale changes need to be implemented, to transform the process from one platform to another. The overall process becomes cost-effective and convenient.

Extensible & Portable

Because of its portable and extensible nature, a lot of cross-language operations can be performed easily on Python. Data scientists opt for many GPUs (Graphics Processing Units) for training their ML models on machines. Python is well suited for this as it is portable in nature.

Python can also be combined with C/C++, .NET components, or Java libraries because of its extensible nature.

Literate Programming

In literate programming, a single document contains explanatory prose, pictures, code, graphs, and other things. The code remains completely executable within the environment. Developers recognize it as writing code”.

Literate programming excels in the following areas:

  • Demonstration
  • Teaching
  • Collaboration
  • Presentation
  • Research

Python fully supports such environments with IDEs such as Jupyter Notebook and Leo.

Summary

Taking into consideration the above-mentioned aspects, Python is being desired by massive programmers. Rather, Python is the main programming language used for research and development in Machine learning. In fact, it has been ranked at the top by Github. With Machine learning patents rising at a rapid rate, this is only set to increase in the future.

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Rahul
rahul@tecocraft.com
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