AI and Machine Learning projects are mostly backed up with a certain skill set. It requires deep research and error-free coding. For this to work, you require a programming language whose syntax is simplified and is less complicated. This is where Python seems to be the most suitable option. It is one of the most approachable languages for beginners.
From development to maintenance, Python can help developers to be more efficient about the software they are building. Some of the benefits that make Python the best fit for Machine Learning and Artificial Intelligence are ease of interface, simplicity, consistency, access to libraries, frameworks for AI and ML, flexibility, and more.
Role of Python in Artificial Intelligence and Machine Learning
This article is going to explore the factors that have accelerated the emergence of the language for the implementation of various AI/ML projects, and how the role of Python in Artificial Intelligence and Machine Learning will play an influential role in the coming decades.
Agility
The flexibility accorded by Python cannot be understated in the slightest. Because of its syntactical nature, which is favourable to human interpretation, Python has an edge over its competitors. The core proposition, however, lies in its unsophisticated, consistent functionality. Tapping into this simplicity developers can work on the implementation of the project rather than going into the technical extent of Python.
The fact that one makes selective changes to the Python code and yet does not require recompilation of the source has cemented its popularity owing to the quick results it yields, unlike programming languages such as C/C++, which require recompilation for the slightest of tweaks. Its intuitive framework allows for a tempered learning curve and affords one to decipher and interpret things far more concisely than would any other programming language. Collaborative development is also rendered a breeze thanks to the abstraction of the complexities usually extant in endeavors of this nature.
An Ever-Increasing Number of Frameworks and Libraries
Perhaps the most important aspect of Python has been the conception of a robust and unremitting stream of frameworks, libraries, and other dependencies from industry and academia alike. This pioneering trait of Python has propelled into stardom, and thanks to the abstraction afforded by libraries and their accompanying functions, the vast majority of which play an indelible role in the rapid development of AI and Machine Learning prototypes, developers can reduce the time taken to undertake some projects considerably.
This probably explains the influx of projects heavily reliant on Python as opposed to any other programming with a similar bent. Furthermore, the extensive profusion of libraries dedicated specifically to projects of AI and ML along with the associated array of substantial documentation with stand-alone examples has allowed even the most elementary of developers (amateurs and early-stage professionals alike) to quickly grasp the rigors of implementing AI and Machine Learning projects through the medium of Python.
This phenomenon has spawned libraries such as OpenCV, NumPy, and TensorFlow that have witnessed an unparalleled degree of espousal by developers within a relatively short period.
Platform-Agnostic Nature of Python
Perhaps the most riveting aspect of Python is the versatility inherent to it. Unlike languages such as C/C++, which are highly platform-specific, Python functions in a rather freewheeling manner. For instance, porting a C code from one machine to another will entail voluminous changes in its code to account for the specificity of the other platform. This balloons development costs and often impedes the passage of quick solutions. However, unlike the former, undertaking such efforts in Python is pretty effortless: only minimal changes are warranted.
This resilient characteristic of Python has been credited as one of the most pivotal reasons undergirding its popularity: development costs can be kept to an absolute minimum and precious resources can be allocated to other considerable ventures. Furthermore, the easy distribution allows for rapid installation in other platforms too, which probably explains why Python code can be easily replicated on other platforms too.
An Active Community of Developers
Any programming language deprived of adequate support and documentation is invariably bound for obsolescence. This unhinging aspect has been vindicated multiple times in the past as well since obscure, poorly-composed documentation throws many logjams in the development process. Given the complexity of contemporary computer applications, developers are not keen on contending with something as trivial as the lack of proper documentation and an active community of developers.
An active forum of developers has often come to the aid of many individuals bogged with problems of an eclectic kind; this often plays an influential role in determining what pace the industry adoption will chart. Be it documentation or the resolution of problems, an active forum facilitates productive interactions and exchanges between developers and novices, bridging the gap between technical know-how and the ability to navigate around problems with ease.
Owing to the aforementioned factors, Python has placed itself in the pole position as the language developers are most inclined towards when it comes to the implementation of AI and Machine Learning projects. Every passing day witnesses the proliferation of exciting frameworks and programs to help individuals acquaint themselves with the salient aspects of this intriguing programming language.
For instance, The IoT Academy has collaborated with leading experts and industrial behemoths to develop advanced certification programs dedicated to the fusion of Python, AI, and ML. Instances such as these are a testament to the enduring legacy of Python and the pivotal role it has played in rendering the implementation of AI and Machine Learning projects a ubiquitous proposition.
The role of Python in Artificial Intelligence and Machine Learning will soar rapidly over time, and as more frameworks are conceived, the vast majority of projects of this kind will be wholly reliant on Python.
How is Machine Learning implemented with Python?
One of the best parts related to Machine Learning is that there is no hard and fast rule on the programming language when it comes to final implementation. As ML is typically used to process large volumes of data, you can go with any of the programming languages. Various Python libraries which you can use for implementing Machine Learning with Python include NUMPY, SCIPY, MATPLOTLIB, THEANO, TensorFlow, KERAS, etc.
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