The choice of language for project development depends upon a lot of factors like personal preference, ease of code, availability of developers etc. Are you confused to choose a language for your next big AI project? The list you compiled might probably include LISP, Prolog, Java, Python, C# and maybe a few more. Most of the Artificial Intelligence projects uses Python for their development. Here we will see the reasons and uses of Python for AI development.
What is Python?
Python is an Oops based, high level, interpreted programming language. It is a highly useful language. It mainly focused on rapid application development (RAD) and don’t repeat yourself (DRY) also. Python works perfectly as a mediator language as well i.e. to connect existing components together. Python is one of the fastest growing languages because of its ease of learning, scalability and adaptability. Python’s support and ever evolving libraries make it a good choice for any project whether Web App, Mobile App, IoT, Data Science or AI.
Reasons of using Python for Artificial Intelligence-
Python is not limited to only one activity. Its growing popularity has allowed it to enter into some of the most popular and complex processes like Artificial Intelligence (AI).
1. Great Library ecosystem-
A great selection of libraries is one of the main reasons Python is the most popular programming language used for AI. A library is a pre-written piece of code that allows users to reach some functionality or perform different actions. Python libraries provide basic level items so developers don’t have to write code for them from the very beginning every time.
Libraries that you can use for AI-
- Pandas- This library is for high level data structures and analysis. Pandas allows merging and filtering of data, as well as collecting it from other external sources like Excel, for instance.
- Scikit-image- This library is used for image processing.
- PyBrain- PyBrain library is used for neural networks, unsupervised and reinforcement learning.
- NLTK- Library NLTK is used for working with natural language recognition, and processing, computational linguistics etc.
- StatsModels– for statistical algorithms and data exploration.
2. Less Code-
AI consists of a lot of algorithms. Python provides ease of testing as compared to its competitors. This language helps in easy writing and execution of codes. Python can implement the same logic with as much as 1/5th code as compared to other Oops languages.
3. Low entry barrier-
Working with AI means dealing with huge data that you need to process in a convenient and effective way. A low entry barrier means developers can easily adopt it. Python programming language looks like the ordinary English language, and that makes the way toward learning simpler. Simple syntax of Python allows you to work comfortably with complex systems, guaranteeing сlear relations between the framework components.
This language is very flexible so using Python for Ai is a great choice. Besides, Flexibility permits developers to choose the programming styles which they are completely comfortable with or even consolidate these styles to take care of various kinds of issues in the most proficient manner.
- It offers an option to choose either to use OOPs or scripting.
- There’s also no need to recompile the source code, developers can implement any changes and quickly see the results.
- Programmers can combine Python and other languages to reach their goals.
- The imperative style consists of commands. With this style, you define the sequence of computations which happen like a change in the program state.
- The functional style is also called declarative because it declares what operations should be performed. It doesn’t consider the program state, compared to the imperative style, it declares statements in the form of mathematical equations.
- The object-oriented style is based on two concepts: class and object, where similar objects form classes. This style is not fully supported by Python, as it can’t fully perform encapsulation, but developers can still use this style to a finite degree.
- The procedural style is the most common among beginners, as it proceeds tasks in a step-by-step format. It’s often used for sequencing, iteration, modularization, and selection.
The flexibility factor decreases the possibility of errors because programmers have a chance to take the situation under control and work in a comfortable environment.
5. Platform Independence-
Python is a versatile language. That means it can run on Windows, MacOS, Linux, Unix and other platforms also. To transfer the process from one platform to another, developers need to implement some small changes and modify some lines of code to create an executable form of code for the chosen platform. Engineers can utilize packages like PyInstaller to set up their code for running on various platforms. This saves time and money for tests on various platforms. So the overall process becomes more simple and convenient.
Python language is very easy to read. Hence any Python developer can understand the code and can change, copy or share it easily. There’s no confusion, errors or conflicting paradigms, and this prompts to more efficient exchange of algorithms, ideas, and tools between AI professionals.
There are also some tools available like IPython.This provides extra features like testing, debugging, tab-completion, and others, and facilitates the work process.
7. Community Support-
It’s beneficial to have a strong community support built for the programming language. Python is an open-source language. This means that there are many resources available for programmers starting from beginners and ending with advantages. Python documentation is also available online as well as in Python communities and forums. Here programmers and machine learning developers discuss errors, solve problems, and help each other out.
Python programming language is absolutely free as is the variety of useful libraries and tools.
Due to the ease of learning, Python is attracting millions of developers to learn this language. In spite of the fact that AI Projects need a very experienced developer yet Python can smoothen learning curve. Due to its extended libraries and active community, it led to be one of the most growing languages today.
Know more about Python uses in Finance, Analytics and Artificial Intelligence at- Using Python in Finance, Analytics and Artificial Intelligence .
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