Python in AI

Python in AI

Artificial intelligence is the intelligence demonstrated by machines. It's a way of making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think. AI is accomplished by studying how human brain thinks and how humans learn, decide, and work while trying to solve a problem, and then using the outcomes of this study as a basis of developing intelligent software and systems. The development of AI started with the intention of creating similar intelligence in machines that we find and regard high in humans. Artificial intelligence is considered to be the trending technology of the future. Already there are a number of applications made on it. Due to this, many companies and researchers are taking interest in it. But which programming language can these AI applications be developed? There are various programming languages like Lisp, Prolog, C++, Java, and Python, which can be used for developing applications of AI. Among them, Python programming language gains a huge popularity. Python is one of the most widely used languages in Artificial Intelligence.

What is Python?

Python is a programming language based on Object-Oriented Programming. It is a highly useful and robust programming language which focuses on Rapid Application Development and DRY(Don’t Repeat Yourself). It perfectly works as a glue language also i.e. connecting to the existing components together. Due to the scalability, ease of learning, and adaptability, Python has become one of the fastest growing programming languages. But the question is -

Why do people choose Python for AI?

Python creators value beautiful design and look. They prefer complex to complicated.  Python has a clean grammar and syntax. It’s natural and fluent. Python’s goal is to be cool in use. This language has a playful approach to many tutorials and other materials. If comparing to other OOP languages, Python is relatively easy to learn. It has a bunch of image-intensive libraries such as - VTK, Maya 3D Visualization Toolkits, Scientific Python, Numeric Python, Python Imaging Library, etc. These tools are perfect for numeric and scientific applications.

Python is used everywhere - in simple terminal commands, in vitally important scientific projects, and in big enterprise apps. This language is well designed and fast. It’s a scalable, open source, and portable.

Developers enjoy the variety and quality of Python’s features. Though it’s not the perfect scientific programming language, it has data structures, classes, flexible function calling, syntax, iterators, nested functions, kitchen-sink-included standard library, great scientific libraries, cool open source libraries such as - Numpy, Cython, IPython, MatPlotLib. It also has other features like - the holistic language design, thought out the syntax, language interoperability, balance of high-level and low-level programming, documentation generation system, modular programming, correct data structures, numerous libraries, and testing frameworks.


The Python community’s focus on providing friendly introductions and ecosystem support to non-programmers has really increased its adoption in the sister disciplines of data science and scientific computing. Countless working statisticians, astronomers, biologists, and business analysts have become Python programmers and have improved the tooling. Programming is fundamentally a social activity and Python’s community has acknowledged this more than any other language. Python’s developer community support and ever-changing libraries make it an ideal choice for developers who want to work on any project whether it is a mobile application, web application, Internet of Things, Artificial Intelligence, Machine Learning or Data Science.

Less coding

There are a lot of algorithms involved in AI. With Python, you can easily write and execute all the codes. Python is capable of implementing the same logic with as much as 1/5th of the code when compared to other OOPS based programming languages.


One of the core advantages of Python is Flexibility. Python is suitable for every purpose with the option to choose between OOPs approach and scripting. It works as a perfect backend and it also suitable to link different data structures together.

So, how to build AI by using Python?

Before to start, you should understand that building AI in Python will take some time. To build AI with Python, you need to understand this language. Of course, it’s almost impossible to reach the ultimate understanding of machine learning in a short period of time. That’s why it’s better to start with gaining basic machine learning knowledge or improving its level. This language is also widely used for machine learning and computing. It Including the needed packages - NumPy, scikit-learn, iPython Notebook, and matplotlib. These Python libraries will be useful when you build AI.


You will use NumPy as a container of generic data. Containing an N-dimensional array object, tools for integrating C/C++ code, Fourier transform, random number capabilities, and other functions, NumPy will be one of the most useful packages for your scientific computing.


The other important tool is pandas, an open source library that provides users with easy-to-use data structures and analytic tools for Python.


Matplotlib is a 2D plotting library that creates publication quality figures. Its advantages are the availability of 6 graphical users interface toolkits, web application servers, and Python scripts.


Scikit-learn is an efficient tool for data analysis. It’s open source and commercially usable. It’s the most popular general purpose machine learning library.


You should also read about decision trees, continuous numeric prediction, logistic regression, etc. If you want to learn more about Python in AI, read about a deep learning framework Caffe and a Python library Theano. Any machine learning project will benefit from using Python. Programming artificial intelligence using Python is efficient. You can share your experiences with us in the comments. Thank you!

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