Role of Python in the Advancement of AI and ML
First released in 1991, Python is an open-source interpreted programming language with a focus on code readability. The main philosophy of python is to allow developers to write logical and clear code for their projects.
In 2021, Python has emerged as the top programming language along with Java and C++. This growth in Python’s popularity can be attributed to the rise of Artificial Intelligence (AI) and Machine Learning (ML).
Artificial Intelligence is a technology that gives machines human-like intelligence. This technology is being utilized across all industries including, marketing, healthcare, robotics, and business analytics. Experts have predicted that when Artificial intelligence evolves, machines will become more capable to make decisions without needing any human intervention.
Machine learning, on the other hand, is a part of artificial intelligence that enables machines/devices to extract new information from the given data to perform specific tasks.
Many developers are making use of Artificial Intelligence and Machine Learning to develop more personalized websites and applications mainly to improve the user experience and engagement. Python has been instrumental in developing flawless AI and ML applications, due to its stability, simplicity, access to vast libraries, and a strong community.
Here is an in-depth understanding of the Role of Python in the Advancement of AI and ML:
Platform independence is the ability of a programming language to adapt to different types of hardware or operating systems. Python is a platform-independent programming language and AI/ML applications developed using Python can run on the Windows, Linux, Unix, Android, and macOS operating systems without needing any modifications or planning. This is extremely useful when developers want to launch their products on multiple platforms, with python they do not need to rewrite the code and test the application once again and can get away by simply altering few lines of code and making small changes in the product. A platform-independent programming language saves the developer’s time, reduces the development cycle, and is extremely cost-efficient.
Simplest Programming Language:
Python is intuitive and easy to learn, therefore, development teams can work collaboratively on a project without any discrepancies. Python has a simple, straightforward, and readable syntax. It helps developers to build reliable applications even though Artificial Intelligence and Machine Learning technologies can work well with more complicated algorithms. This quality of python can help developers focus more on the AI/ML solution instead of working out issues in the programming language. It is also great for developing prototypes quickly and testing them to check for issues. Many developers prefer working with Python as it does not have a steep learning curve and makes it easier for them to work collaboratively with other developers.
Access to Great Libraries:
One of the main reasons why Python has emerged as the best language for AI/ML applications is its extraordinary selection of libraries. These libraries are extensive and enable developers and designers to build applications and add features without needing to write code from scratch. Machine learning requires devices to keep on generating and process new information with the help of the available data and Python libraries enable developers to access and alter information when required.
The main Python Libraries used for ML and AI applications are:
- StatsModels: Used to conduct statistical tests and carry out data exploration.
- Pandas: Great for analyzing high-level data structures. Allows developers to combine, collect and classify data from external sources.
- Matplotib: used to create two-dimensional plots, tables, histograms, and other data visualization formats.
- Keras: Deep learning framework that provides simple and consistent API and actionable error messages to developers.
- Scikit-learn: Manages ML algorithms including clustering, preprocessing, dimensionality reduction, model selection, and regression.
- Natural Language Toolkit: Natural Language Toolkit or NLTK is used for Python applications that can make use of human language data.
Comes with Visualization Tools:
Data visualization tools represent data in a graphical manner to help users identify patterns, trends, and outliers. Few Python libraries are great visualization tools. For instance, data scientists can use Matplotib to create plots, histograms, and charts for data understand and effective presentation. To develop applications with AI and ML functions it is important for developers to have access to data showcased by these visualization tools in an innovative as well as an understandable format. These visualization tools make it easier for developers and data scientists to keep their reports to their clients more transparent
Python is a very versatile language that allows you to go for object-oriented programming or scripting. This programming language also supports functional, imperative, and procedural programming styles along with the object-oriented paradigm. AI applications developed using Python as less likely to have any major errors as the programmer/developer can work comfortably by going with their usual programming style. When working with Python developers do not need to re-compile code and can easily make changes to the system. Python is well-suited to the rapid development of AI/ML applications because it is not limited by templates or complex APIs.
Strong Community Support:
Python has surpassed popular programming languages like Java in terms of community support in 2021. There are various online forums and platforms that share machine learning solutions for developers, making it easier to get some guidance during AI/ML projects. Due to this community support, you can avoid common Python mistakes while working on your project and find readymade solutions to your issues by communicating with other Python developers online.
Undertaking AI/ML projects is quite challenging as it is, it requires developers to conduct deep research and pick the right tech stack. The quality of AI-backed products depends on whether the programming language being used is flexible, reliable, and has a variety of tools available. Python fulfills all these requirements and is thereby considered the ideal language for developing AI and ML applications. If you have a good understanding of developing AI and ML applications with Python, chances are you will get more projects and better opportunities as a software developer or a programmer in the job market. In addition to Python, languages like Scala, R Lang, and Java may also dominate the AI/ML landscape.
Author Bio: Poorvee Kalyani Alla is a mass communication graduate, a copywriter, and an avid blogger who writes on technical and lifestyle-related topics for West Agile Labs, Exegy Consulting, Jewellerista, etc.
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