Reasons For Using Python For Artificial Intelligence

Why are More Developers Using Python for Their Machine Learning Projects?

The global Artificial Intelligence market is calculated to boast $190.61 billion in 2025. This suggests the massive use of the technological and software developments being integrated into the execution of AI. And Python development services being ranked as the ever upgrading best programming language will hold an integral post in AI’s future. AI development services fuelled real-world applications, programmed with the ease of Python, are soon going to revolutionise the life of millennials.

Considering such significant roleplay between AI and Python, it’s time to understand their relationship in detail. Therefore, Let’s begin.

Comparison of Python with other programming languages

Python versus C++

  • According to several Python consultants, Python is much easier to learn and execute as compared to C++
  • Python has many libraries that are helpful in data analysis.
  • Python requires less coding and is a dynamic language. Therefore, it can smoothly collaborate with other technologies.

Python versus Java

Although Java is faster to run and easier to debug statistically typed and compiled language, Python has surpassed it in ranking because of its ease of use and easy to read. Therefore, although technically Java should be the first choice for curating high-end applications, Python is the preferred choice for many Python development companies and developers.

Reasons for using Python for AI

Now that we have understood why Python is better than C++ and Java for artificial intelligence, it’s time to comprehend the reasons why any AI development company use Python for AI.

  1. Huge library system
    A huge library system is a prime reason why Python is the choice of a huge number of developers. We mean the pre-written codes that can easily be used to execute a particular function or action by the term library. This saves a lot of time for AI developers, which otherwise would get wasted in curating even the basic codes from scratch.
  2. Minimal coding
    AI has multiple algorithms to deal with. And Python helps in ease of testing as compared to other programming languages. Python requires just 1/5th of the codes, as used by other languages, to execute the same task in AI.
  3. Easy to adopt
    When developers deal with AI, they need to work on a huge amount of data conveniently. Python provides a low entry barrier to the developers, which means they can easily adapt it, to fulfil their needs. The Simple syntax of Python helps to solve complex problems most efficiently, thus ensuring clear relations between frameworks.
  4. Flexibility-
    The flexibility of the language allows the Python developers to choose the style that they are comfortable in. Apart from this, the various perks of Python as attached to its flexibility attribute are:
     Developers have the option to choose from OOPs or scripting.
    – No need to re-compile the base code. All the changes are immediately visible when implemented.
    – Python can easily be combined with other languages if needed.
    – The procedural style of Python is used for sequencing, modularisation, iteration, and selection.
    – The flexibility factor reduces the risk of errors.
  5. Platform Independence-
    Python is a versatile language and thus can be used on any platform, including Windows, macOS, Linux, Unix and other platforms as well. All the Python web development company needs to do is make minor modifications in codes under the chosen platform, and thereafter the entire transfer process becomes convenient and simple.
  6. Readability-
    Python language is very easy to read and comprehend. Developers can easily copy it, share it or implement changes in it. It is devoid of any confusion or conflict of paradigms. As a result, there is a peaceful exchange of algorithms, tools, and ideas between AI professionals.
  7. Community Support-
    Python has strong community support. Being an open-source programming language, its documentation, communities and forums are easily available online. Online forums allow experienced developers and even amateur developers to discuss their concerns with each other.

Popular Python library for AI

  1. scikit-learn
    Scikit-learn Python AI library is used by a Python development company to make the implementation of ML algorithms easier. This library is built using NumPy, SciPy and matplotlib libraries. Its algorithms classify the objects, build regressions and group identical objects as a set, pre-processes data and even compare or choose the ultimate model for the developers.
  2. TensorFlow
    TensorFlow is created by Google to let the most complex algorithms accessible to a large segment of developers. It can be used to build data-flow graphs with each node representing a particular computation written in Python language. TensorFlow computation, though written in Python, is executed in C++, thus resulting in increased speed.
  3. XGBoost
    XGBoost is useful for gradient boosting. It helps to classify data and build regressions by the use of decision-tree algorithms. This library improves the scalability and performance for keeping up the program’s growth.
  4. NLTK
    NLTK stands for Natural Language Toolkit. This library is helpful with trivial linguistics. The interfaces and functions of this AI library are helpful in tokenizing and tagging text, too. This general-purpose NLP library can be used in any language-based project.
  5. spaCy
    spaCy has been titled by developers as “the Ruby on Rails of Natural Language Processing.” It is extremely simple, fast, and efficient. It helps to easily comprehend all aspects of a text, and also pre-process it for other libraries to be used in the future.
  6. Gensim
    Gensim makes the identification of topic modelling much easier. It easily manages the entire modelling process with no need to load the entire text into memory.
  7. FANN
    FANN means Fast Artificial Neural Network Library. This library makes use of artificial neural networks in Python and other different languages for creating, training and running artificial neural networks in three function calls. It is featured with incredible documentation, comprehensive framework and versatility. A substantial library to employ neural networks.
  8. ffnet
    Ffnet implements feed-forward neural networks and makes use of a graphical user interface for visualising datasets. It is also featured in automatic data normalisation, thus saving a lot of time in the pre-processing of workflow.
  9. PyTorch
    PyTorch is developed for the execution of computation tasks and durable neural networks. The PyTorch neural network need not be recreated every time a change is implemented. It is mainly used to replace NumPy with GPUs and for deep learning research platforms.
  10. OpenCV
    Open Source Computer Vision Library (OpenCV) is used by developers for multiple computer vision cases, like face detection and recognition for classifying human actions. It has extensive community support and allows extensive documentation. A perfect library for adding computer vision infrastructure to any project.

Final words

Python has innumerable AI libraries to meet the demand of different cases. Python AI libraries make robotics and expert system development much easier and accessible to all Python developers. As a result, it reduces the work pressure and improves the speed, thus saving time. Python plays a significant role in AI coding by providing a good framework and easy-to-use visualisation tools. Apart from this, its super-fast prototyping makes it the most popular language as compared to its competitors.

AI involves an immense amount of research. This means there is no need to waste time and effort as compared to the heavy boilerplate codes of Java to test the hypothesis. If Java is used, the deadlines of the project can never be met. Whereas the use of Python will validate the code in 2–30 lines. Thus proved, Python is the best programming language for A, despite the high cost attached to it.

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