Why Python Is An Ideal Choice For Accomplishing Projects Involving AI & ML


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Artificial Intelligence (AI) and Machine Learning (ML) are two advanced technologies currently trending in the domain of computer science. Engineers have ample opportunities for using different programming languages for accomplishing AI and ML-based projects.

But what makes Python gain an edge over others for being the most extensively used high end interpreted programming language for projects involving AI and ML. Let’s take a deeper look at this in the article.

Which Programming Languages Are The Backbone For AI & ML Projects?

Projects involving advanced technologies such as AI and ML are usually accomplished successfully with the help of LISP which is a popular high-level programming language. Being an interactive, powerful, and expressive platform LISP is well-suited for such projects in a software development company.

Uniform syntax, dynamic typing, and garbage collection are few of its features which make LISP a perfect fit for these projects. Also, it’s highly flexible and provides programmers with great freedom to make use of their creative side and experiment.

Prolog is another popular programming language for projects involving AI and ML. Its built-in unifier and is blessed with flexible frameworks. Also, it supports tree-based data structuring and pattern matching which are the two major mechanisms mandatory for seamless AI programming.

In addition to these two, there are a few other languages that are suitable for AI and ML projects. For example, C/C++ and Java are also appropriate for such projects. But Python is similar to LISP that has outshined all others and has become the ideal choice for programmers to execute projects involving technologies such as AI and ML. Why is it so? Let’s explore the core reasons.

AL & ML With Python: Why Is It A Deadly Combo?

Python was introduced in the late 1980s and was named after a popular British comedy group Monty Python. It’s precise and complex as well as gifted with clean grammar and syntax. This language is equally beneficial for both startups and industry giants.

Its built with a primary focus on beautiful design and impressive look. Also, Python is a highly scalable and portable platform. Coming directly to the point, let’s look at some of its major features which makes it an ideal alternative for projects involving AI and ML:

  1. Highly Flexible Platform

Python is extremely flexible as its suitable for every purpose and allows the engineering to choose between OOPs approach and scripting. This language is the best for linking data structures together. Also, it has a perfect back-end and acts as a lifesaver for programmers who are stuck between different algorithms by providing them with a power to check the produced code in the IDE itself.

  1. Availability Of Prebuilt Libraries

Python has several libraries in stock which makes the process of reiteration seamless for the developers. They can easily pick a library in accordance with the specific requirements of the project.

For instance, this language offers comprehensive libraries such as SciPy for advanced computing, Numpy for scientific computation, and Pybrain exclusively for machine learning. Also, programmers can save a lot of their precious time by using ‘Modern Approach’ which is a unique Python library that prevents them from the tedious and time-consuming task of coding base level items.

  1. Platform Independent Nature

Python is a language that makes the entire process of building solutions functioning seamlessly on multiple platforms a breeze for programmers. By tweaking the code, developers can make apps ready to run on a different OS. This ultimately saves a lot of time which programmers would have spent on testing apps on different platforms.

  1. Do More With Less Code

In Python, programmers can implement the same logic with less code as compared to the coding required by other programming languages. This language streamlines the entire process of writing and executing the code. Also, it allows developers to use the interpreted approach to verify the code simultaneously while producing it.

  1. Immense Popularity

Being highly flexible and versatile Python has a short and simple learning curve. Also, Python professionals can be found very easily compared to searching for Prolog or LISP developers. The passionate Python community makes sure to improvise the code at regular intervals of time and make it a better platform.

  1. Extensive Support

Python is an open source platform which is backed up with an enthusiastic community of experts and professionals. This community provides Python developers with all the essential resources they need to work quickly in a hassle-free manner. Also, the community experts are always ready to support and rescue amateurs in each and every phase of the development lifecycle.

How To Get Started With Building AI & ML Using Python?

One thing programmers must always remember that building AI and ML in Python isn’t a piece of cake. It’s a time-consuming process which depends upon the programming experience, skills, and enthusiasm a developer possess.

In order to get started with the process, you need to grab a good command over its fundamentals. The very first step is to install Python and look at it as a language which is extensively used for AL, ML, and advanced computing. Let’s check out the two major steps for accomplishing this process:

  1. Install the open source analytics platform of Python, the Anaconda.
  2. Add the essential packages for machine learning such as iPython Notebook, NumPy, matplotlib, and scikit-learn, etc.

Here, NumPy is a container of generic data which is the best for scientific computing. It offers random number capabilities, an N-dimensional array object, Fourier transform, tools for integrating C/C++ code, etc.

  1. Pandas is another open source Python library that provides the developers with analytic tools for the language and easy-to-use data structures.
  2. You can also refer to Matplotlib which is used for creating quality figures for publication. It’s a 2D plotting platform which is blessed with Python scripts, web application servers, and 6 graphical users interface toolkits.
  3. For data analysis, you can explore Scikit-learn which is an open source efficient platform. Also, it’s the most well-known general-purpose ML library that can be used commercially.
  4. In order to gain a detailed knowledge of Python with AI, you can look at a Python library Theano or a comprehensive deep learning framework Caffee.

EasyAi, AIMA, SimpleAI, and pyDatalog are a few popular Python AI libraries. Along with this, PyML, PyBrain, scikit, and MDP are the ones specifically for ML.

In addition, you can refer to a few of the following books in order to enhance your Python knowledge base and sharpen your skills:

  1. Learn X in Y Minutes
  2. Google Developers Python Course

III. An Introduction to Python for Scientific Computing

  1. Python The Hard Way

It’s a good idea to refer to the comprehensive documentation of Python time and again to stay updated and informed. Now move to the second step of enhancing your machine learning skills. It’s not possible to be a pro in this field very quickly. But it’s good, to begin with grabbing the basic acquaintance with the theoretical concepts of ML by exploring one of the following two courses:

  1. An Introduction to Python for Scientific Computing
  2. Andrew Ng’s Machine Learning Course

This is how you can start building AI and ML using Python and can master the art of working with two of the most advanced technologies. Being blessed with a simple syntax and seamless readability, Python is the only language that allows developers to focus on fixing problems and fulfilling project objectives.


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