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Why is Python used in Artificial Intelligence(AI) ? Here are the top 10 reasons:

In the  beginning of your coding journey, you may find yourself asking –

“ What exactly is a programming language ? ”. As we know , to communicate with a person we need a specific  language. 

If you want to teach someone ,we need a language i.e., understandable by both communicators. Similarly, to communicate with computers, programmers also need a specific type of language , this type of language is known as “Programming Language” .  

There are tons of programming languages out there that allow you to do all sorts of things, from building virtual reality experiences to creating video games , creating user-friendly web applications to beautiful mobile appliactions or making a computer a human-understandable robot using Artificial Intelligence. 

Here, we discuss  Artificial Intelligence and introduce some points on Python’s strong side, and find out why you should opt  for Python when bringing your AI to life projects . 

Python is a foolproof  language that feels like reading uncomplicated English . The pseudo code nature of python makes it easy to learn and understandable for beginners .

Let’s some benefits of using Python for Artificial Intelligence and Machine Learning :

A great choice of libraries is one of the main reasons Python is the most popular programming language used for AI.

A library includes a pre-written piece of code that allows users to reach some functionality or perform  different actions.

 Some most widespread libraries you can use in Python for AI:

  1. Sci kit-learn for handling basic ML algorithms .
  2. Pandas for supremel data structure and analysis.
  3. Keras for deep learning (fast calculations and prototyping ).
  4. TensorFlow for working with deep learning by setting up , training and utilising AI neuron networks with massive datasets  .
  5. Matplotlib for creating 2D plots.
  6. Numpy stands for “Numerical Python”. It is a commonly used  library. It is a popular machine learning library that supports large matrices and multi-dimensional data. It consists of in-built mathematical functions for easy computations .Even libraries like TensorFlow use Numpy internally to perform several operations on Tensors .
  7. Scrapy is an open source library that is used for extracting data from websites. It provides rapid web crawling and high level screen scraping. Scrapy can also be used for data mining & automated testing of data.
  8. PyGame is an easy interface to the Standard Directmedia Library(SDL) platform-independent graphics , audio , and input libraries .It is used for developing video games using computer graphics and audio libraries along with Python Programming Language .
  9. PyTorch is the largest machine learning library that optimises tensor computations. It has rich APIs to perform tensor computations with strong GPU acceleration. It also helps to solve applications issues related to neural networks.
  10. PyBrain stands for Python Based Reinforcement Learning , Artificial Intelligence , and Neural Networks Library .It is an open-source library built for beginners in the field of Machine Learning. It provides rapid and simple  algorithms for Machine Learning tasks. It is so flexible and easily understandable and that’s why it is really helpful for developers that are new in research fields. 

Working in the ML and AI industry means dealing with a bunch of data that you need to process in the most convenient and effective way.

It allows  data scientists to quickly pick up Python to a great extent and start using it for AI development without  wasting vigorous effort on learning the language .

Python Programming language resembles the everyday English Language , and that makes the process of Learning easier. Its simple syntax allows you to comfortably work with complex systems , ensuring clear relations between system elements.

Every python developer can understand the code of their peers and change , copy or share it .There’s no confusion, errors or conflicting paradigms, and this leads to more efficient exchange of algorithms, ideas, and tools between Al and ML professionals. 

There are also tools like IPython available, which helps in  testing , debugging, tab-completion, and others, and facilitates the work process.

It is most important and helpful during development of any AI projects . A lot of Python documentation is available online as well as in Python communities and forums , where all developers discuss errors and solve problems .

Python is becoming more and more popular among data scientists , according to stackOverflow , the popularity of Python is predicted to grow until no other opponent comes .

When you use Python for AI and ML development , we will access numerous predefined packages .Therefore , Python doesn’t require you to indulge in hard core coding because you already have predefined packages .

Since python is a legible programming language , the formulas can be executed rapidly . Machine learning  requires long training sessions and these training sessions can last for days even. Nonetheless, Python has a faster execution speed which is pivotal .

One of the main reasons  companies hire expert Python developers is that they offer flexibility .Python as a flexible platform allows developers to choose from different programming language styles.

As a result , depending on the developer’s needs , they can also consider combining various styles.

Python provides the benefit of practicable code . AL and ML require solving complex algorithms. However, the clarity of Python will ensure that developers can easily write the codes. 

Not all programming languages are platform-independent. However, Python, a versatile  programming language , doesn’t benefit platform independence . Python can easily function on different platforms such as unix , Linux , macOS and Windows etc . To transfer the activities from one platform to another, developers need to execute several small-scale changes and modify some lines of code to create an workable form of code for the chosen platform. Developers can use packages like PyInstaller to prepare their code for running on different platforms. Again, this saves time and money for tests on various platforms and makes the overall process more simple and convenient.

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