Python For Artificial Intelligence

The concept of intelligence is at once both controversial and complex depending on the levels of its perception. While humans are considered to be the most intelligent being in nature today, computers being a device inverted by humans for easy calculations, in the beginning, researches has been elongated to its own intelligence today. One of the definitions of intelligence complies that it is the ability of the subject to consciously be able to perceive its environment and be able to interact and respond to it rationally which a human can do naturally. Besides rationality on the verse of intelligence comply that doing the right thing, through experience and learning. Intelligence is a special characteristic gifted to the human being and artificial intelligence is a property that human has been trying to put into the computer system from the last fifty years and the journey is still continuing. Research over languages, libraries, algorithms alongside ontology, neural networks, speech processing has been conducted giving out various positive results.

On the verse of programming language, python is the most diversified and used language for artificial intelligence especially data science and machine learning. Created in 1991 by Guido Van Rossum, Python is an interpreted high-level programming language that supports a wide variety of programming paradigm across various domains, and today especially over artificial intelligence and data science. Here is the rundown for why python is widely preferred for the domain of artificial intelligence. 

Caption:Python 

Source:Facebook Engineering

1. Python has a great prebuilt library supporting statistical analysis and applied data science which is the core of artificial intelligence. Data visualization is the major beginning for any statistical analysis within machine learning and data science, for which python also provides great visualization libraries. Beside python supports libraries for deep learning, NLP, and so on. Widely used and effective python libraries are shown here:

Widely Used Python Libraries in AI:

Python Library	AI domain	Functionality
Scikit-learn	Machine Learning	Provides a lot of efficient tools for machine learning and statistical modeling including classification, regression, clustering, dimensionality reduction and so on.
Pandas	Data Structure	Provides expressive and flexible data structure
Seaborn	Data visualization	Provides high-level interface for drawing informative and attractive statistical graphs
NumPy	Computation	Provides advanced math functions and scientific computing packages
TensorFlow	Machine Learning, Deep Learning	To create large-scale neural networks, and used for classification, Perception, prediction and so on with various machine and deep learning models
Theano	Deep Learning	Allows to evaluate mathematical operations including multidimensional arrays
Matplotlib	Data visualization	Used for data plotting and graphing for further analysis
SciPy	Machine Learning	Provides numerical routines for optimization and numerical integration
Keras	Machine Learning (Neural Network)	Provides mechanism for expressing, processing and compiling neural networks
PyTorch	Machine Learning, Deep Learning	Allows to perform tensor computations, create dynamic computational graphs and calculate gradients automatically and offers rich APIS for issues related to neural networks too.
LightGBM	Machine Learning	Provides high-performance gradient boosting framework based on decision tree algorithm, used for ranking, classifications
Eli5	Machine Learning	Allows to visualize and debug machine learning models using unified API
NLTK	Natural Language Processing	Natural Language toolkit providing text processing libraries for tokenization, parsing, classification, stemming, tagging, semantic reasoning and so on.
Lasagne	Deep Learning	Provides functions to build and train neural networks over Theano

2. PyInstaller within python libraries makes the language platform-independent and developers can easily prepare their code for running on different platforms.

3. Along with its growing popularity, the community support is another reason for its use s it helps to encounter errors and correct them easily.

Python has a moderate learning curve with a very low entry barrier. These mechanisms in python has led to the use of this language in the field of artificial intelligence today for building various AI models.