Wondering why Python is the best fit for artificial intelligence in web development? Let’s discuss the nuances in detail.
As Artificial Intelligence (AI) evolves, we see trends favoring Python. Python is already a popular language with a 15.2 million-strong community. If Stack Overflow is to go by, the language is expected to grow at 27% per year from 2022 onwards. Guess what? The language’s fastest-developing utilization is in artificial intelligence.
Python and artificial intelligence are growing in parallel with each other. Developers are leveraging the simplicity of Python to create complex AI systems to map inputs, analyze data, and solve problems accordingly. So, next time you interact with an AI system, spare a thought for the language that makes it functional and endearing.
But why do Python and AI go hand-in-hand? Well, to get your head around that, you first need a fair understanding of Python.
Call it a general-purpose, interpreted, open-source, object-oriented, code-readable, dynamically typed, and portable programming language with a convenient syntax structure. Here’s what these jargons mean.
With the basics of Python out of the way, it’s time to explore why Python is the best fit for artificial intelligence in web development. Here’s your rundown.
Python takes the pain out of coding. The code in Python is short and simple, which makes it easier to pull off complicated algorithms and diverse workflows involved in AI. You can devote your resources to finding solutions for machine learning issues rather than dealing with the complexities of the programming language. As a side note, the same logic in any other language would involve 5X longer code.
Even if you are a beginner, Python could quicken your progress in machine learning. Python is an interpreted language, allowing you to run the code immediately upon writing it. Any bugs can, thus, be rectified there and then, saving you a lot of time. The language also allows for seamless collaborations where developing a code can be a team effort. Just generate an URL for collaboration and you are sorted.
Well, a vast community means great support. You can turn to Python enthusiasts for help at any stage of the software development life cycle. The language has its own community where you can share, learn, interact, and collaborate. Even hiring and partnering with like-minded professionals is possible. An email newsletter, Python Weekly is there to keep you on top of the latest developments in Python.
Libraries are a set of pre-written, reusable codes, alongside documentation, data, values, templates, and more. They simplify and accelerate application development, execution, and integration. Python has plenty of them, many of which are tailored for ML. NumPy, TensorFlow, Theano, Keras, Scikit-learn, and Pandas, you name it.
Regardless of the issue, you have a corresponding Python library for it. Just install a library that suits your needs and execute the algorithm you have created in Python. Since the need for writing the code from scratch is out of the way, you will end up improving efficiency and productivity and saving plenty of time.
Here’s a lowdown on the best Python libraries for AI, ML, and Text Processing:
There’s a need for story-telling in AI to make data comprehensible for users. The human brain processes visuals 60K times quicker than text. Information presented in graphs and charts, thus, is more readily consumed than that contained in reports and spreadsheets. That’s where Python kicks in, providing a vast set of libraries and modules for data visualization. With histograms, graphs, and plots easily creatable, you can present and demonstrate information for deriving business intelligence.
Take, for instance, Matplotlib. You can draw on this plotting package to come up with static, interactive, and dynamic 2D graphs. From NumPy to Pandas, it works well with all key Python libraries. Likewise, Seaborn can help you create bar charts, pie charts, histograms, and scatterplots with a few lines of code. Seeking something more versatile? Well, Plotly can come to your rescue with chart types like multiple axes, line charts, scatterplots, bar charts, box plots, sparklines, dendrograms, 3-D charts, and more available. Other options include Geoplotlib, Bokeh, and Folium.
Cross-platform compatibility is another reason why Python is the best fit for artificial intelligence in web development. A given Python application can be run on a variety of operating systems with minor tweaks in the source code. A Python application for iOS works perfectly on Windows (or vice-versa) with minimal rework. With the need to rewrite, test, and move codes eliminated outright, Python comes across as a big time-saver and productivity booster. That’s a bonus, especially when developing those AI systems that involve a lot of time and effort. Again, you have access to libraries like PyInstaller to expedite code creation for various platforms.
Freedom to do more comes by default with Python, which is a godsend when working on AI and ML. As a dynamically typed language, it offers you ample room to experiment when building a particular feature. There’s simply no need to bother about onerous rules. Whether to go with a scripting approach or opt for an OOPS approach, Python gives you complete freedom here as well. Also, expect a great degree of flexibility in problem-solving, which is the cornerstone of AI. Developers even count on Python to combine different data structures to good effect.
Python’s open-source environment is ideal for AI development. As the source code is free, anybody is free to download, modify and pass around the modified version of the code. It’s compliant with the Open Source Initiative, which means the usage is unrestricted without any membership costs. As an open-source language, it can be integrated with C++, Java, Rust, and various other popular languages. Guess what? The Python code comes backed by documentation, which renders it a high degree of maintainability. That means it will work as expected.
With machine learning gaining traction, Python is exploding in popularity. It simplifies and speeds up the complexities of AI development.