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Machine learning is a process to build AI enabled algorithms with which machines are able to learn or produce codes automatically through analyzing the given data. Machine learning is the subset of Artificial Intelligence and again has the intersection with many fields including math and psychology. Now after giving a brief introduction let’s start with the tech part of the article: After doing intensive research, I clustered these following languages, but please don’t be afraid to learn the other programming languages because to become a competent programmer and data scientist you must know a dozen of tools to stumble upon one that works the best in a particular situation, hence you can’t restrict yourself to a language or two. Again to mention different jobs are best done in different languages.
1) R Language: This language was developed to as a modern version of S language developed in Bell labs, R language is combined with lexical scooping, which tends to provide the flexibility in producing statistical models. R is a really powerful language to start with machine learning, as there are many specified GNU packages available. One can surely choose to use R for creating powerful algorithms and plus the R studio has an easy statistical visualisation of your algorithms. Though the language is widely used in academic research and gaining really well recognition in the industry use most recently.
2) Python Python language is one of the most flexible languages and can be used for various purposes. Python has gained huge popularity base of this. Python does contain special libraries for machine learning namely scipy and numpy which great for linear algebra and getting to know kernel methods of machine learning. The language is great to use when working with machine learning algorithms and has easy syntax relatively. For beginners, this is the best language to use and to start with.
3) C language: The mother of all language is definitely a great programming language to build your predicative algorithms. Developed at Bell Labs by Denise Ritchie (Turing Award winner Computer Scientist). This language is not a cakewalk and should be only be considered when you have strong fundamentals of computer science and programming languages, however, once you are proficient in C language then there is nothing that can stop you developing your advance algorithms. One does not need Ph.D. but knows the computer programming concepts thoroughly. You can build your own regressions analysis and time series simulation easily, which would create strong machine learning algorithms. In conclusion, I would like to add that there are many other languages that you can use after going through the above ones. Once you get deeper you can explore the functional languages like Haskell, Erlang , Julia and Scala, these tools need you to have good knowelge of C first. As a beginner, you can start with Python and move to other languages once you get the command of that.