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Best programming language for Artificial Intelligence

Every language is beautiful in itself be it English, French or Latin. Same it is when comes to programming. There’s a lot to choose from. Some popular programming languages are Java, C/C++, Python, Go, C#, PHP, JavaScript and the list goes on.  Programming languages broadly falls in two categories i.e. Object Oriented languages like Java, Python, C++ etc and Functional programming languages like C. When it comes to choosing a language, a lot of factors influence the choice. Mainly popularity in industry and availability of jobs are the decisive ones. 

Popularity of Artificial intelligence is growing exponentially. After 2010, due to advancement of computing technologies and availability of Big Data, Machine learning and Deep learning algorithms became more powerful and focus shifted from academia to its practical applications. 

Technologies cannot be restricted to any particular programming language. Languages like Python, Java, C++, JavaScript, R etc all are used in AI but their usage is heavily dependent on specific use case.  

Artificial Intelligence algorithm involves a lot of Mathematics and computational resources. Additionally, they require a lot of data which also needs to be stored and processed accordingly. So the language chosen must provide useful features to deal with these requirements. When it comes to best programming language for AI and related technologies, Python outshines the rest. As of 2021, Python is most widely used programming language in the world. It recently dethroned Java to get this badge. Python is the first priority of AI practitioners around the globe & used by majority of Data Scientists.  Python has a lot to offer which makes it the best choice for AI. 

Python is an easy to learn language even for naives with no programming background. Python code resembles English language which makes is easy to learn & understand. It is not mandatory to end statements by semi colon (;). Python uses white space/indentation to define blocks as compared to braces ({}) in other languages. All this makes the code look cleaner as well as reduce the actual lines of code to do the same task if compared to some other language.

Availability of diverse Python libraries makes AI practitioner’s life way easier. Even writing AI algorithms from scratch is very easy as compared to Java or C++. Python data structures are very helpful and provide flexibility required for handling large data sets easily. This is further enhanced by supporting libraries like Pandas which makes handling of structured data very easy. Various libraries make it easy to read and pre process the data before feeding it into the ML models for training or predictions. 

Libraries like SciPy and Numpy has many pre-implemented mathematical functions which are used frequently. TensorFlow, Keras and PyTorch are the three most popular Deep Learning frameworks for python. There are many Open Sourced pretrained models available in these libraries which help in transfer learning and quick implementations. They are widely used in industry for making production level models. Scikit Learn is another popular library containing majority of machine learning algorithms ready to use with many helper functions helpful in data cleaning, outlier detection and removal and much more. 

Visualization libraries like Matplotlib, Plotly and Seaborn help in crating various data visualizations. Interactive graphs can also be plotted using these libraries which help in describing properties of data. Python has rich collection of other libraries and web frameworks like Flask and Django which are useful in creating API’s and integration in web and edge devices. 

Python’s active community keeps updating it with new features and maintains it properly. Its simplicity, easy to learn syntax and vast library support makes it most popular choice of both the professionals and beginners in AI and Data Sciences. 

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Written by HackerVibes

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