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Today, In this article, we’ll discuss why Python is widely used for AI and ML.
With each passing minute, AI and machine learning are grabbing more eyeballs than ever. Who’d have thought that there could exist a self-driven car or smartphones that forecast what weather it will be tomorrow! But today, all this is a reality.
AI projects are different from traditional software projects. There is a difference between the technology stack, the skills required for an AI-based project, and the need for in-depth research. To implement your AI aspirations, you must use a programming language with stable, flexible, and available tools. Python offers it all, so today we see a lot of Python AI projects.
From development to deployment and maintenance, Python helps developers be productive and confident about the software they create. Benefits that make Python the best fit for machine learning and AI-based projects include simplicity and consistency, access to the best library and framework for AI and ML, flexibility, platform independence, and a wide community. These add to the overall popularity of the language.
Why is Python widely used for AI and ML?
Top reasons to make Python popular for AI and machine learning.
Rich library ecosystem
A programming language library is a reference to a module that comes with pre-written code that allows the user to use the same functionality to perform different actions. Python has libraries that help developers save time because they don't have to start from scratch.
List of some common libraries used for AI and machine learning:
- Pandas
- Scikit learn
- Keras
- TensorFlow
- Caffe
- PyBrain
With the help of these libraries, AI and ML algorithms can be implemented more easily. These libraries are useful for data analysis, in-depth learning, machine learning, computer vision, and advanced computing.
Alterableness
Python is an alterable (flexible) language, meaning it can be used in conjunction with other programming languages to achieve the desired results. This gives the developer the option to choose between OOPs or scripting. Also, it does not need to retrieve the source code, which will make it easier to see the results. Due to its flexibility, it provides a safe environment for the developer.
Simple and consistent
Python offers concise and readable code. While behind complex algorithms and versatile workflow machine learning and AI, the simplicity of Python allows developers to write reliable systems. Developers have to do their best to solve the ML problem instead of focusing on the technical details of the language.
In addition, Python attracts many developers because it is easy to learn. Python code can be understood by humans, making it easier to build models for machine learning.
Many programmers say that Python is more intuitive than other programming languages. There are many other frameworks, libraries, and extensions that facilitate the implementation of various functionality. Since Python is a general-purpose language, it can perform a complex set of machine learning tasks and enable you to quickly build prototypes that allow you to test your product for machine learning purposes.
Platform freedom
Platform independence refers to a programming language or framework that allows developers to execute things on one machine and use them on another machine without any (or at least minimal) changes. One of the keys to Python's popularity is that it is a platform-independent language. Python is supported by a number of platforms, including Linux, Windows, and macOS. Python code can be used to create standalone executable programs for most common operating systems, meaning that Python software can be easily distributed and used on those operating systems without a Python interpreter.
What's more, developers often use services like Google or Amazon for their computer needs. However, you can often find companies and data scientists who use their own machines with powerful graphics processing units (GPUs) to train their ML models. And because Python is platform-independent, this training is much cheaper and easier.
Great Community & Huge Popularity
Having strong community support helps the programming language in many ways, especially when it is an open-source language. Python is free, comes with useful libraries and tools, and its documentation can be accessed online. Programmers can discuss their problem statements in forums and have conversations with others to find solutions.
Industrial use of python to implement AI & ML
Various industries use Python to implement AI in their products and services. Travel, fintech, transportation, and healthcare: Python is used. There are some well-known companies that use AI to provide A-grade services to their customers. Industry giants like Uber, Tesla, JPMorgan Chase, Apple, and others have adopted this technology.
You need to know about Siri. One of Apple's best-known products is AI. It uses voice recognition tools and NLP to answer questions and give instructions when performing various actions.
Facebook has been taking advantage of the power of AI for many years. They use it to find maps, fake accounts and report inappropriate content.
How You Can Miss Microsoft Cortana! The tech giant used AI to create an assistant called Cortana. It is available in Windows 10. As the company claims ‘your personal productivity assistant’, Cortana helps you focus on the important things and achieve more with less effort.
Volkswagen, a big name in the automobile industry, the German brand is now replacing human drivers with self-propelled car technology using AI algorithms. Pittsburgh-based autonomous automaker Argo AI has teamed up to develop a car that allows the driver to drive in specific conditions without having to rely on wheels.
Conclusion
Computer-based intelligence or artificial intelligence is profoundly affecting the world we live in, with new applications rising steadily. Brilliant designers are picking Python as their go-to programming language for the various advantages that make it especially appropriate for AI and profound learning ventures.
Python’s broad choice of AI explicit libraries and structures disentangle the improvement procedure and cut advancement time. Python’s basic grammar and comprehensibility advance fast testing of complex calculations and make the language open to non-developers.
It likewise lessens the psychological overhead on engineers, opening up their mental assets with the goal so that they can focus on critical thinking and accomplishing venture objectives. Lastly, the straightforward punctuation makes it simpler to work together, or move extends between designers.
Python additionally flaunts a vast, dynamic network of designers who are glad to offer assistance and backing, which can be important when managing such complex ventures.
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