Why is Python considered a good language for AI and Machine Learning?

Python has enjoyed a steady rise to fame over recent years and is now jostling for the position of one of the most popular programming languages in the world. Favoured for applications ranging from web development to scripting and process automation, Python is quickly becoming the top choice among developers for artificial intelligence (AI), machine learning, and deep learning projects.

The high-level, robust programming language focuses on rapid application development. The core functionality has helped Python become one of the fastest-growing programming languages. Here are top reasons why developers and companies working on emerging technologies including AI, and ML tend to choose Python.

Simple code structure:

Python focuses on reducing the number of lines of code that you write to execute a function. The language simplifies the code and makes it readable. The combination of Python and AI will help in reducing the size of code that developers write. The language, if used with AI and ML algorithms, will make it easier to reduce the quantity of code.

Vast Community:

Python is well-known for its extremely vast community. Since its release, the community is contributing largely and which is the reason a Python user never feels abandoned with sudden changes. The constant upgrade by the developer community support makes Python one of the most suitable languages for machine learning applications. Large organisations such as Google, Amazon, Facebook, etc. are using this language.

Frameworks & Libraries:

One thing the developers like most about Python is the abundance of open source libraries and frameworks. The language has a great number of machine learning libraries and some of the prominent libraries are such as TensorFlow, Pytorch, Matplotlib, SciKit Learn, etc. Python has a collection as well as code stack of various open-source repositories in almost every domain such as Django for integrating web applications, pandas for machine learning, SciPy for scientific computing, Librosa for audio, OpenCV for images, NumPy for text, etc. Other commonly used libraries for artificial intelligence and machine learning are such as pyDatalog (Logic Programming engine in Python), PyML (the bilateral framework is written in Python that focuses on SVMs and other kernels methods), EasyAI (Simple Python engine for two-players games with AI), PyBrain (simple and effective algorithm for ML tasks), AIMA (Python implementation of algorithms), simple, etc.

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