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5 Key Advantages of Using Python for Artificial Intelligence Development

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27th Feb, 2020
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5 Key Advantages of Using Python for Artificial Intelligence Development

While HTML/CSS and JavaScript were the most commonly used programming languages as of 2019, Python is seeing a steady rise to fame and jostling for the first position in the list of the most preferred programming languages for artificial intelligence (AI) and machine learning (ML) projects. Learn more about the top data science programming languages.

AI is on the onset of creating a technologically advanced world, with Netflix and Spotify already leveraging the technology to recommend TV shows/movies and artists/songs to their users. AI is also making its way in industrial processes to enhance process workflows and employee productivity.

Now, AI has surely become a revolutionizing technology and as more and more companies look towards implementing it in their everyday processes, there comes a need for a programming language that can make it easy to develop projects on AI.

This is where Python comes in. Given that it has a pretty simple syntax and can easily handle difficult processes, Python has made its way to becoming one of the best programming language for ML & AI.

Let’s see what makes Python the most-preferred choice for AI development.

Advantages of Using Python for AI

Python is an outstanding language majorly because it doesn’t need compiling into machine language instruction to be executed. A developer can directly run a program written in Python.

But other than this, there are a lot more benefits of choosing to develop AI projects using Python.

1. A huge library ecosystem

Python offers a vast choice of libraries for AI development, which contain base-level items that save coding time. These libraries also make it easy to access, handle, and transform data.

Here are some of the most widespread Python libraries that are used for AI:

  • Scikit-learn: It takes care of basic algorithms like clustering, linear regressions, classifications, etc.
  • Pandas: It helps with merging and filtering data for high-level data analysis.
  • Keras: It ensures fast calculations and prototyping by using the GPU as well as the CPU of a computer.
  • TensorFlow: It helps with deep learning programs by using artificial neural networks with massive datasets.
  • Caffe: It allows switching from CPU to GPU. It can also process over 60 mn images in a day using NVIDIA K40 GPU.

2. High readability

Python is famous for its compact, readable code, and is practically unmatched with regards to usability, especially for new developers. This has made it a preferred language for AI and deep learning. Learn why developers prefer python.

AI depends on incredibly complex calculations and multi-stage work processes, so the less a developer needs to stress over the complexities of coding, the more they can concentrate on discovering answers for issues, and accomplishing the objectives of the venture.

Python reads like our everyday English language, thus making AI development easier and less complex.

Python’s concise syntax implies that it requires less coding time that most other programming languages, and permits the developer to rapidly test algorithms without executing them.

Also read: Learn python online free!

Moreover, easily comprehensible code is priceless for collective coding, or when AI ventures are shared between different development groups. This is especially valid if a venture contains a lot of custom business logic or outsider parts.

3. The flexibility of the language

Python for AI is an extraordinary language, as it is truly flexible:

  • It offers a choice to pick from using Object Oriented Programming (OOPS) or scripting.
  • There’s no compelling reason to recompile the source code; developers can actualize any changes and observe the outcomes.
  • Software developers can join Python and other languages to achieve their goals.

Besides, flexibility permits developers to pick the programming styles which they are completely comfortable with or even join these styles to tackle various kinds of issues in the most productive manner.

  • The imperative style comprises commands that portray how a PC ought to play out the given commands. With this style, you characterize the sequence of calculations.
  • The functional style is additionally called so in light of the fact that it declares what tasks ought to be performed. It doesn’t consider the program state. Unlike in the imperative style, it declares proclamations as mathematical equations.
  • The object-oriented situated style depends on two ideas: class and object, where similar objects create classes. This style isn’t completely supported by Python, as it can’t completely perform exemplification, yet developers can utilize this style to a limited degree.
  • The procedural style is the most widely recognized among first-time developers, as it continues errands in a bit by bit position. It’s frequently utilized for sequencing, iteration, modularization, and selection.

The flexibility of Python diminishes the plausibility of blunders, as software developers get an opportunity to take control of the situation and work in a comfortable environment.

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4. Abundant community support

Python is an open-source programming language and is supported by a ton of assets and top-notch documentation. It additionally flaunts a huge and dynamic network of developers ready to give guidance and help through all phases of the development procedure.

A strong developers’ community can be of great help while using python for AI development. A ton of Python documentation is accessible online just as in Python communities and forums, where software engineers and AI designers talk about mistakes, take care of issues, and help each other out.

Python programming language is totally free just like its assortment of valuable libraries and tools.

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5. Excellent visualization options

upGrad’s Exclusive Data Science Webinar for you –

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As mentioned before, Python has an extensive set of libraries and some of them offer amazing visualization tools. This is of great use in AI as it involves the representation of data in a human-readable format.

Matplotlib is a library for data scientists that allows them to make charts, histograms, and graphs to present data in a more comprehensible and visualized manner.

In fact, making clear reports is also easy with various application programming interfaces that come with Python.

Read our popular Data Science Articles

Final Thoughts

AI is a fast-growing technology that has revolutionized the way scientists develop solutions for real-life problems. They prefer Python for AI development for the following reasons:

  • Python offers an extensive library ecosystem
  • Python is amazingly flexible
  • Python is easy to read
  • Python offers abundant community support
  • Python has a variety of visualization options

Python’s broad selection of AI libraries and systems streamline the development procedure and cut development time. Python’s basic syntax and easy readability add to quick testing of complex calculations and make the language accessible to non-developers as well.

Python likewise lessens the subjective overhead on designers, opening up their psychological assets so they can focus on critical thinking and accomplishing venture objectives. At last, the basic syntax makes it simpler to work together or transfer projects between developers. It flaunts an enormous, dynamic network of developers who are glad to offer assistance and backing, which can be priceless when managing such complex activities.

While other programming languages can likewise be utilized in AI ventures, there is no escaping from the way that Python is at the forefront. This is the reason you should consider Python for your AI venture.

If you are reading this article, most likely you have ambitions towards becoming a Python developer. If you’re interested to learn python & want to get your hands dirty on various tools and libraries, check out upGrad’s Data Science Courses.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1Which one is better for AI- Golang or Python?

In terms of readability, Python enjoys popularity, but it may go overboard at times. Python does provide you with numerous methods to communicate the same idea, which might lead to misunderstanding. In terms of programming, Golang, on the other hand, adheres to rigid guidelines. It does not enable the simple import of unneeded libraries or the creation of superfluous variables. When compared to Python, Golang offers more scalability and performance possibilities. The speed of Go is superior to the speed of arithmetic computation, which is why it was chosen. When compared to Python, it can handle up to 20-50 times more difficult arithmetic problems and do so much quicker. Obviously, this means Golang has a greater chance of succeeding at a task.

2What are the limitations of Python for AI?

Python's primary drawbacks include its slowness during execution, difficulties transitioning to another language, inability to build mobile applications, excessive memory consumption, and lack of appeal in the corporate development industry. Python is not built for mobile environments, and it is widely acknowledged as a poor language for mobile computing. In comparison to other commonly used technologies such as Java Database Connectivity, Python's database access layers are not yet established. Python is a dynamically typed programming language, thus run-time mistakes are quite frequent. Python's database layers are also weak. As a result, it is not often used in large businesses with large database requirements.

3Are there any disadvantages of Artificial Intelligence?

One of the most serious shortcomings of artificial intelligence is that it is gradually replacing humans with bots for a variety of monotonous activities. Many career possibilities have been lost as the necessity for human intervention has decreased. Another one of AI's major drawbacks is that it can't learn to think outside the box. AI is capable of learning over time using pre-fed material and prior experiences, but cannot be innovative in its approach. It takes a lot of skill to develop a machine that is capable of imitating human intelligence. It takes a lot of time and energy, and it may be quite expensive too. AI also requires the most up-to-date technology and software to stay current and fulfil the most stringent standards, making it extremely pricey.

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