Who would’ve thought that machines will fix appointments, suggest songs, and decide the best route...basically add ease and efficiency in our lives. You guessed it correct, machine learning and artificial intelligence is what will drive the present towards a more efficient and user friendly future. These technologies enhance search functionality, personalization, and provide smart recommendations. User eXperience has been redefined and now creates immense value across industries.
Why use Python when bringing the above mentioned experiences to life. Let us take a look at some of the top reasons for doing so.
Why Is Python Preferred Choice in AI & ML?
The tech stack for ML & AI varies a lot and is different when compared to the traditional software development projects. There is a need to carry out deep research and the right set of skills. The programming language that you use should have the necessary tools, be stable, and flexible at the same time. Python offers the programmers a required set of tools that help them from development to deployment and then the maintenance. Other such benefits that make Python a preferred choice in ML & AI are consistency, simplicity, flexibility, access to libraries and frameworks for AI and ML, and a big community.
1. Easy to Use
Programmers work with complex tools, but believe us...no one prefers things to be complicated. Python offers an easily readable syntax and this makes it a preferred choice for experimental students and experienced developers at the same time. Rather than spending their time and resources to understand the technical jargon and nuances, the developers can now focus on the problem at hand and effectively solve it. Efficiency in Python helps developers write fewer lines of code while completing more work at the same time. Developers can easily understand the python code and this makes it easier for them to further craft efficient machine learning models.
2. Multiple libraries and Frameworks
In the last 30 years, Python has proved to be a great tool in the hands of developers. There have been many changes, additions, and upgrades overtime. Now there are hundreds of libraries and frameworks that the developers can use. These wide ranges of libraries and frameworks help developers to save a lot of time and energy when working on ML & AI projects.
Some of the libraries that are specifically beneficial in the machine learning and artificial intelligence environment are TensorFlow, Keras, and Scikit-Learn. The best part is that these three libraries are open source and developers can experiment these with other networks as well.
3. Not Platform Dependent
In programming terms platform independence is a feature in a framework that allows developers to implement changes on one machine and then use them on a different machine without or minimal changes. Python is a platform independent language and is supported by several platforms such as Mac, Linux, and Windows. So there is no need to use an interpreter when working with python language as it can be used to create standalone executable programs that can be used on different platforms with ease. Developers use Amazon or Google for their computing needs, and there are still many programmers who use their own powerful machines with heavy GPUs when training their machine learning models. As python is not dependent on any platform, it helps in the training being cheaper and much easier.
4. Great Community
Python is among the top 10 programming languages in the world. This also means that apart from ease of programming, the developers can easily find the help and support they are looking for when working in this framework. When building your AI based project, you can easily find the appropriate framework to support you. In the last 30 years, many have learned and are learning python programming, and so it is also easier for companies to find developers or development companies when there is a huge project. There are several forums, and a great contributing online community that keeps adding new and informative material on machine learning. This furtherance of technical know-how helps in creating more innovative and efficient solutions.