Artificial intelligence (AI) is a growing field of software development, and machine learning (ML) is part of AI – machine learning takes data and algorithms and uses them to help AI learn and improve.

When it comes to programming for AI and ML, Python is a powerful and versatile language. Thanks to human-friendly syntax, Python has become one of the top coding languages for ML and AI.

If you’re ready to take your programming career to the next level and be part of the evolving world of machine learning and artificial intelligence, then learning Python is one of the best things you can do for yourself.

But what makes Python so good for AI? Let’s take a look at how it works.

Is Python Good for AI?

Yes! Python is powerful, scalable, and readable, making it almost tailor-made for complex AI and ML models. Unlike traditional software projects, AI programs and ML algorithms require a unique technology stack, specialized skills, and extensive research. Python empowers developers to go further, from development to deployment and maintenance.

Reasons Python is a Good Programming Language for AI

As a coding language, Python offers many advantages to its users. Those advantages translate into making it one of the best, most powerful, and most popular programming languages for AI and ML.

Great Libraries

Python works so well for AI and ML thanks in no small part to its exceptional library ecosystem. These libraries, offered by sources like PyPi, contain pre-written code modules that eliminate the need for developers to start from scratch each time they write a new program. Python’s enormous library system facilitates the development process, allowing users to focus on ML and AI functionality rather than writing repetitive code.

This becomes especially valuable in ML, where continuous data processing is essential to functionality. With Python, the ML and AI possibilities are endless, as the language offers a library for almost every action developers require in their AI and ML endeavors. Some popular ML and AI libraries and their functions include:

• NumPy, a numerical library and probably one of the most popular libraries for writing Python for AI
• SciPy, for scientific and technical AI computing
• Theano, based on NumPy and used to construct deep learning models
• Pandas, for data analysis
• PyBrain, for ML tasks

Of course, many more Python libraries are available that cater specifically to AI and ML coders. The ones listed here are just a few examples of how programmers can find the necessary tools.

Flexible

One of Python’s trademark qualities is its flexibility. That flexibility offers developers the freedom to choose between object-oriented programming (OOP) and scripting. This also allows Python to integrate seamlessly with other languages; for example, you can write part of your code in Python and the rest in C++.

Python empowers developers to choose from various programming styles based on their specific needs. This versatility gives Python an edge as the language of choice for AI and ML, enabling developers to create impactful applications easily. Some common Python styles include:

• Imperative: good for making sure code is executed according to a desired flow and actions are performed in sequence
• Object-oriented: good for modeling real-world entities, such as a banking system
• Functional: good for concurrent programming
• Procedural: good for using Python to automate tasks

Readable

Thanks to its understandable syntax, Python allows for seamless collaboration and rapid development among ML and AI programmers. Python code is easily understood, modified, and copied. There’s little (if any) confusion or conflicting paradigms, thereby allowing a more efficient exchange of algorithms, ideas, tools, and more between programmers. Even when joining an ongoing project, developers can quickly grasp what’s going on. Additionally, Python offers optimization capabilities for testing, debugging, and streamlining the development process.

Scalability

Python is simple enough to build an AI or ML platform on a small scale and then make it bigger and more complex as the need arises. This way, developers can write and test their work quickly before adding on. The entire development process becomes more efficient because problems are solved on a smaller scale, which is ideal for AI or ML projects requiring a fast turnaround.

Versatile & Platform Independent

The term platform independence” refers to a programming language or framework that allows developers to implement things on one machine and use them on another device without any (or with minimal) modifications. Unlike some programming languages