Python is one of the world’s most popular and widely used programming languages – and with good reason. It excels in many areas, including video game creation, mobile development, and even embedded programming. Another area that Python shines is Artificial Intelligence (AI) and machine learning (ML).
Python for Artificial Intelligence and Machine Learning
You may ask yourself why so many companies rely on Python for AI versus similar language, such as Perl, C#, or even C. Part of the reason, as you may suspect if you are familiar with Python, has to do with its powerful abilities when it comes to pattern recognition and predictive analytics. That alone, however, is not enough to justify Python’s place at the head of the table when it comes to AI.
To truly answer this question, developers have to delve a little deeper. For starters, most popular AI frameworks prefer Python’s development capabilities, despite the fact that AI is supported by many other languages, including C++, C#, R, Go, Scala, and others. Python, however, has the lion’s share of AI documentation, libraries, and community support.
Read: Introduction to Machine Learning in Python
Python AI Features
Below is a list of Python features for AI that might explain why it is so widely used for artificial intelligence and machine learning functions. We will explore each of these features and how they relate for developers briefly in the following sections.
Python AI Libraries
The Python programming language has a bounty of built-in libraries and modules – perhaps more than other other developer languages. In fact it is one of the most common reasons why programmers choose to code with the language. As you might suspect, many of those libraries (both built-in and third-party or community supported) revolve around both AI and machine learning.
Some popular Python AI libraries and frameworks include:
- TensorFlow: A high-level Python-based neural network and deep learning library.
- Scikit-learn: A Python-based library for machine learning, data mining, and data analysis.
- pylearn2: Python-based library which is more flexible than scikit-learn.
- NumPy: Python-based library for data science, scientific computing, and multi-dimensional array objects.
- SciPy: a Python-based library considered the first choice for mathematics and engineering.
Word in the developer community is that the most widely used deep-learning frameworks are being programmed with Python and C/C++ under the hood. However, Python, due to its recompiling nature, can allow immediate results, and allow for Object-Oriented Programing (OOP) features, code prototypes, and scripting-based programming, giving it even more of an edge over the C-family of languages when it comes to AI and ML applications.
Read: Machine Learning vs Deep Learning: What is the Difference?
Easy Implementation
The ease and immediate implementation of Python qualify it for OpenCV. There is also no need to gain in-depth knowledge in Python to start using it for ML and AI. Some basic knowledge of Python will qualify even newbie developers and data scientists to deal with AI. In addition, developers need less time to write and debug their Python code, which makes it valuable for new AI students who wish to focus more on AI algorithms rather than dealing with code or syntax errors.
Python is simple to learn and code in, which is one reason IT professionals – and those outside of the IT realm – choose it as their first language. Many people from various careers (such as mathematicians, physicists, and statistical specialists) are not able to learn C++. But they can learn Python because of its English-like syntax. This allows non-programmers to create models much simpler and focus on pure algebra and statistics versus the ins and outs of a programming language.
Readability
As stated above, Python code is very similar to plain English. It is easily readable even for non-programmers. This also simplifies writing code and learning it. Its syntax is not complex and it is even much easier for machine learning and AI development purposes.
Tutorials and Resources Available
Python has grown in large part thanks to the vast array of resources and free tutorials available to beginner (and veteran) developers. It is fairly simple to find beginner tutorials for new programmers and perhaps just as easy to find resources when you run into complicated problems that you cannot solve or debug on your own.
Read: Simple Image Recognition and AI with Python
Python Data Tools
Python also attracts data scientists because it has a full stack of data tools. It is well known, of course, that data is the backbone for AI and feeding machine learning. Comparing Python with the R language for entrance showcase a huge difference, despite R being another popular choice for AI programing. R seems like a domain-specific language in the world of AI, while with Python, you have rich, full-featured tools to obtain visualizations and determine patterns in a single language.
Large Developer Community
When it comes to the community, there is no other language that can defeat the popularity of open-source Python. Either at an individual or institutional level, there are plenty of packages and levels of support for your development activities. The big community and ecosystem around Python is a result of its ease of use and power; that same community from its side increases Python’s popularity and growth. The community is a great place to find like-minded individuals, jobs, mentorship, forum discussions, troubleshooting help, and documentation.
OS Platform Independence
In addition to its versatility, Python is a cross-platform language, which means it can run on practically any platform. This has contributed to it being the best candidate for machine learning and AI development. Companies need to run AI on all platforms; Windows, MacOS, Linux, Unix, and many more. There is no need to implement huge changes to transfer the Python code from one platform to another. In addition, there are also some automatization packages, such as PyInstaller, to make the code run more smoothly on the target platform.
Conclusion
Python has great capability for AI and machine learning development. It has many unique features that rarely are seen together in other programming languages. From its intuitive syntax and basic control flow down to its data structure support and libraries, Python is hands-down the best language for prototyping AI algorithms.
Moreover, Python includes everything AI engineers can imagine: rapid prototyping, a diverse standard library, multi-paradigm, performant numerical libraries, open-source machine learning libraries, and more. This puts it in the foreground of tools for machine learning, statistical calculations, soft computing, NLP programming, and even smart web scripting.
