The artificial intelligence (AI) market is expected to be worth USD 16.06 Billion by 2022, growing at a CAGR of 62.9% from 2016 to 2022. The base year used for this study is 2015 and the forecast period is from 2016 to 2022.- MarketsandMarkets.com
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Now that the paradigm has taken a 180 degrees turn, simple problem-solving approach has taken a back foot and complex algorithms and heuristics are playing major role. However with the constant evolution of machine learning a strong and versatile programming language is what developers look for. MATLAB, R, Python, Java, ELM, etc., have gained immense popularity as programming languages, but which one is the most promising language for machine learning among all? and why?
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Comparing top five machine learning languages
Let’s do a comparative analysis of these languages:
|Basis of Comparison
||Extreme Learning Machines
|What are they?
||Matrix-based language used for computational mathematics
||Purpose-built language used for statistical computing
||General-purpose language used for bringing production systems into operations
||Functional programming language used for extensive coding
||Highly functional language used for developing user-interfaces
|Best suited for
||Representation and execution of matrices
||Comprehend and explore statistical data
||Integration and web development
||Development of applications
||Developing web front-ends
||Built-in graphics and comprehensive development environments
||Numerous machine learning algorithms present by default
||Broad range of libraries and user-friendliness
||Accuracy and precision
||Standard library full of graphic and text tools
||Complex syntax and lesser perfect packages
||More fragmentation reduces productivity
||Inefficient architecture code
||Not useful for developing large apps
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Who is the winner?
Though all the aforementioned languages are suitable for different use cases, a developer should particularly know which language to choose when it comes to machine learning. According to a report published back in 2015, R programming was considered to be the most preferred programming language for analytics, data mining and data science purposes. However Python has made a strong impact on the data scientists over the past few years gaining the dominance of 75% adoptability in comparison to other languages.
Moving ahead from being a simple data processing tool Python has now become a leading data analytics tools with 61% developers using Python as their primary language for coding, as per PyCharm.com.
Moreover a research conducted by Indeed.com shows the growth trends of 9 different languages, where Python has come out to be the most demanding programming language for machine learning following R on the second place and Java on the third. Though both R and Python are growing exponentially but with improved analytical tools and more activities Python is gradually displacing R.
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Reasons behind Python’s Success
Python being a general-purpose language supports diverse activities like web development, artificial intelligence and data analytics which in turn has made it gain momentum among the programmers.
As per TIOBE index, Python is the 4th most popular programming language out of 100.
However it is important to know the factors driving its success, which are:
- User-friendly – Python is easy to understand and allows developing prototypes and tools easily and that’s why more and more programmers choose Python as their primary programming language.
- Flexible – Python is quite flexible as there are no such rules on developing features.
- Easily understandable – Being a high-level language the syntax of Python is simple which helps programmers to write programmes easily.
- Great demand – Python is one of hottest technologies in the market offering high paying jobs to the professionals, i.e., around $94,093. (Source : Gooroo.io)
- Extensive libraries – Python has huge machine learning libraries allowing it to be most suitable in this area.
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Which language do you want to choose?
The selection of programming language depends on the use-case and using a strong language is utmost important for developing a high-end software product.
While a developer having a in-depth statistical knowledge may find R Programming Introduction to be the best fit, an individual having a good grasp of programming may find Python or Java suitable. Hence it is extremely important for you to first analyze the kind of use-case you are dealing in and then opt any programming language.
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