Machine learning and artificial intelligence-based projects are currently trending in the IT domain. Python provides ample opportunities for accomplishing AI and ML-based projects. It expands developers’ abilities and capacity of artificial intellect and machine learning programming that enhance the user experience and create value across many industries.
But you might think what makes Python gain an edge over other most extensively used programming languages? So, let’s first understand what Python is?
Python can be defined as a highly performed, robust, OOPs based high-level, interpreted programming language. It also works very well as a glue language that connects existing components together. Python’s simple, easy to learn syntax emphasizes scalability and adaptability, makes it one of the fastest-growing and cost-effective languages. Python’s support and open-source library options make it a good choice for any developer to build apps, whether it is Mobile App, Web App, IoT, or AI.
The reason that makes Python the best programming language for machine learning and for artificial intelligence?
In terms of the technology stack, skill sets, and the necessity of deep research, AI and Machine Learning Algorithms are different from traditional software projects. Python offers stable and flexible programming language that makes any project more reliable, it’s why we find lots of Python AI projects today. It makes developers confident and productive about the project from the development to deployment they’re building.
Its simplicity, access to the great library, consistency and frameworks, and wide community makes it a good fit for AI and ML-based projects.
Python offers a compact and readable code and allows developers to write reliable systems. Instead of focusing on the technical refinements of the language, Python helps developers to solve issues related to Machine Learning Algorithms. Its understandable codes make it easier for developers to build models for ML.
Flexibility – Its flexible language makes it a more reliable choice for machine learning
According to the requirement, it allows developers to choose either OOPs or scripting. Without recompiling the source code, it allows developers to implement any changes and quickly can access to see the results.
Developers can combine other languages with Python to reach their goals.
It enables developers to use a programming language according to their choice and comfort. Also, they can combine these styles to solve different types of programming problems in the most efficient way. It decreases the possibility of errors, as a programmer gets a chance to work on their preferred language in a comfortable environment.
A huge collection of libraries and frameworks –
An extensive group of libraries and frameworks are one of the main reasons that has made Python a popular programming language. As implementing AL and Machine Learning Algorithms can be tricky and requires a lot of time, having a number of Python frameworks helps developers to reduce their development time. Python provides a pre-written code library that developers use to solve their common programming tasks.
Some of the extensive set of libraries are –
SciPy for advanced computing
Scikit-learn for handling basic Machine Learning algorithms
Scikit-image for image processing.
Keras for deep learning
Pandas for general-purpose data analysis
StatsModels for statistical algorithms and data exploration
Matplotlib & Seaborn for creating 2D plots, charts, and other visualization forms
Platform independency –Python programming language is very versatile as Python for machine learning development can run on any platform including Windows, Unix, macOS, Linux, and others. Developers just need to implement several small-scale changes and a few modifications in codes to transfer the process from one platform to another. It helps to save plenty of time and money and make the overall process convenient and cost-effective.
Great community and popularity –It supports building the programming language. As an open-source language, Python enables developers with a bunch of open resources during the entire project. Online and in Python communities and forums, a developer can find a lot of Python documentation, where programmers and machine learning developers can discuss their hurdles, errors, solve problems, and enable each other to help out. Its reliability is making it more and more popular among data scientists across the globe.
Summing it all –
AI and ML are the fastest-growing universal technologies that help developers to come up with clever solutions.