With the rise in AI applications across industries, it is vital for businesses to adopt a coding language that simplifies the creation and execution of code. Python has emerged as a popular choice amongst users to develop AI, data science, and Machine Learning (ML) models due to its flexibility, ease-of-use, abundance of libraries, and a strong community of developers. Python’s reputation as a preferred language for these domains has grown significantly in recent years.
This article aims to compare Python programming language with other commonly used options in the realm of Artificial Intelligence and Machine Learning, while exploring the reasons behind Python’s superiority in these fields.
Here are some key factors that establish Python as the preferred language for AI and ML projects:
The abundance of libraries and frameworks available
Developing applications using artificial intelligence and machine learning technology can be a demanding and time-consuming task. Thankfully, Python is backed by a vast and varied library of resources, making it a top pick for many developers. The wide assortment of libraries available plays a pivotal role in the popularity of Python, enabling developers to create more efficient and powerful applications with minimal resources.
Please note: To employ the work of other developers, one can import libraries into the editor and use the library’s function(s).
Machine Learning (ML) is an effective tool that has gained significant traction in recent years, and the Scikit-learn library provides a comprehensive range of ML techniques that can be leveraged for optimal results. This library encompasses various techniques, including linear regression, logistic regression, and support vector machines. In addition, there are several other libraries at the disposal of Python programmers that make coding more efficient and quicker, such as spaCy, NLTK (Natural Language Toolkit), TensorFlow, PyTorch, and Keras, which are commonly used in the Artificial Intelligence (AI) domain. Moreover, additional data manipulation libraries like NumPy, Pandas, and Seaborn enable Python programmers to enhance their programming potential.
Python has an uncomplicated syntax that is reminiscent of the English language.
Python’s syntax is intuitive and bears resemblance to conversational English, making it simpler to grasp and work with, sparing engineers valuable time. Furthermore, Python’s implementation of indentation instead of brackets results in a neater and more structured syntax, thereby making it easy to comprehend.
Code recompilation is unnecessary.
Employing a static library eliminates the need for frequent recompilation of code, enabling software engineers to implement changes with ease while observing their impact. Python’s ability to facilitate such effortless adjustments is one of its significant benefits.
Python code functions on a diverse range of systems, such as Windows, Mac, UNIX, and Linux.
Exceptional community support
Python is a popular programming language, thanks to its open source nature, which enables a vast community of users to make contributions. The community is recognised for its amiable approach to new users and readiness to welcome contributions from all levels of proficiency. This ensures that any issues or bugs in the program are quickly detected and resolved.
Python’s indentation feature enhances code readability, as discussed in this article on feature scaling in Python.
Python and its Philosophical Foundations
Tim Peters’s The Zen of Python articulates the principles that guide Python, including the following.
- Beauty is the opposite of ugliness.
- Explicitness is preferred over ambiguity.
- Generally, simplicity is preferred.
- Complexity is better than simplicity.
- Flatness is preferred over nesting.
- Sparse is preferred over dense.
- Readability is crucial.
- Exceptions should not be made without good reason.
- However, practicality beats purity.
- Wrong actions should always have consequences.
- Except when silence is required.
- Avoid making assumptions in uncertain situations.
- One clear approach is required.
- However, that path may not be immediately clear without being Dutch.
- Today is preferable to tomorrow.
- The present is usually the ideal time, but there are times when never is preferable.
- An implementation is not effective if it cannot be easily explained to others.
- It can be a clever decision if the implementation method can be described with ease.
- Introducing more namespaces is a great idea because of their effectiveness.
Comparing Python with Other Programming Languages
There is no denying that Python is a preferred language by developers who work on Artificial Intelligence (AI) and Machine Learning (ML) programs. According to the annual Python Developers Survey conducted by JetBrains and the Python Software Foundation, 28,000 unique visitors from the Python development community participated, of which 85 percent used Python as their primary language and the remaining 15 percent used it as a secondary language.
Although Python’s runtime is not as quick as Java’s, writing a program in Python takes significantly less time because it requires fewer lines of code. Typically, Python code can be up to five times shorter than Java code, and Python programmers do not need to specify the type of arguments or variables, which can save a lot of time.
To comprehend an operation such as a+b, Python interpreter needs to identify the type of variables involved, unlike Java, which only requires the names of the variables. This process contributes to longer compilation times in Python compared to Java. Furthermore, Python allows operator overloading for custom use cases, which is not feasible in Java. Hence, Python is invariably regarded as a “glue” language, offering users greater flexibility.
The documentation for Java can be duplicated using C++. However, Python code is significantly more efficient, requiring 5-10 times less code as compared to C++. Furthermore, Python is commonly utilized as a ‘glue’ language, making it possible to integrate various C++ components.
This article has delved into the numerous benefits of utilizing Python for Artificial Intelligence and Machine Learning. It has been compared with other commonly used programming languages and found to be a feasible option for enterprise-level AI software development. Python’s advantages comprise readability, scalability, and the availability of several libraries and frameworks specially designed to simplify the development of AI and Machine Learning applications. These benefits have made Python a popular choice for developing AI-powered software and have contributed to its substantial growth in the industry.