Artificial Intelligence (AI) is rapidly changing how we live and work, and Python has emerged as one of the most popular programming languages for building AI applications. With its simplicity, versatility, and robust libraries, Python has become the go-to choice for developers who want to develop AI applications quickly and efficiently. In 2023, as AI advances and transforms industries, businesses & organizations need to understand the advantages of using Python for their AI projects. More and more organizations are getting into developing AI-based applications. And this has made it vital for developers to use a programming language that helps reduce complexity in coding and allows easy implementation. The emphasis is on using Python for AI, data science, and ML. The reason is Python’s easy-to-use and flexible tools, its libraries, frameworks & the vast community of developers.
Python is a general-purpose programming language for creating various programs and a wide range of applications, software and web development, automation, and data science. Python primarily focuses on code readability, is open source, offers extensive libraries, and supports user-friendly data structures. It’s considered the best language for building AI applications because of its rich set of libraries for machine learning and artificial intelligence.
Artificial Intelligence defines machines with intelligent programming that collect information from outside of themselves and perform actions based on inputs received. It is a technology that mimics human intelligence and allows computer applications to learn from interactions or interactive processing, and steps are performed based on these inputs.
I’ll explore the benefits of using Python for AI development and discuss why it is an excellent choice for organizations looking to leverage AI in their operations.
What is the Advantage of using Python for AI?
Libraries and Frameworks
Implementing AI and ML can be tricky & it’s essential to have a well-structured and well-tested environment that enables developers to generate consistent coding solutions. With their collection of functions and pre-written codes available, Python libraries save the time and effort of building codes from scratch and provide developers with the fundamental components required for AI and ML development. Some of Python’s extensive set of libraries include –
- Keras, TensorFlow, and SciKit-learn can be used for ML.
- NumPy can be used for high-performance scientific computing and data analysis.
- SciPy can be used for advanced computing.
- Pandas are used for general-purpose data analysis.
- Seaborn used for data visualization.
- NLTK is one of the most used libraries for machine learning. NLTK is mostly used for natural language processing.
- AIMA-Python library implements algorithms from Russell and Norvig’s Artificial Intelligence: A Modern Approach.
- Simple enables the user to implement various algorithms from Artificial Intelligence: A Modern Approach. It is easy to use and has a simple interface.
- Neurolab is a simple library that provides the basic functionality of neural nets. It uses pure Python and Numpy libraries to build algorithms.
A platform-independent programming language or framework allows developers to implement code with minimal or no changes on multiple platforms. Python can be used on any platform, including Linux, macOS, Unix, Windows, etc. Standalone executable programs can be developed using Python, which can be distributed and used on different operating systems without needing a Python interpreter.
Python’s syntax is easy and resembles everyday English. It helps reduce the learning curve for developers and makes it easy to understand and implement the syntax. Python uses indentation instead of brackets, which reduces complexity and makes it easy to understand.
Community Support System
The Python Software Foundation and the global Python community are huge and work to improve its core features and functionalities while actively helping and supporting other programmers in real time. This support may be the biggest reason to choose Python for business. The extensive network of the Python community is always there to provide support to developers.
Python is a flexible language that can be used for various tasks and easily switch between scripting and object-oriented programming. It functions well in the backend and can integrate various data structures.
Another reason for Python’s success and use in AI is that it is diagnosed and not compiled, which means that Python applications can interpret code line by line at runtime and do not have to be collected before runtime. This helps developers build applications faster and helps identify errors at runtime, simplifying debugging.
Python is expected to keep up its position as one of the most popular languages because of its user-friendly syntax, adaptability, and wide range of extensions, and is expected to gain more popularity, especially in the technology sector.
AI and ML are evolving fields and their implementation across industries is helping improve productivity and effectiveness. New applications are developed regularly for and with AI, and it’s beginning to impact our daily lives significantly.
Python has become the choice of language for AI and ML since it’s easy to understand, making data validation quick and practically error-free. Because of access to a widely developed library ecosystem, developers can efficiently perform complex tasks without extensive coding. Python’s versatility makes it easy to perform tests on any platform. Software development services of Python can have a significant impact on companies foraying into AI and AI-related work.