Machine learning is a process to build AI-enabled algorithms with which machines can automatically learn or produce codes through the analysis of given data. Machine learning is the subset of Artificial Intelligence and again intersects many fields, including mathematics and psychology.
Now, after giving a brief introduction, let’s start with the technical part of the article:
After conducting intensive research, I grouped the following languages, but please do not be afraid to learn the other programming languages because to become a competent data scientist and programmer, you must know about a dozen tools to find one that works in the best way. a particular situation, therefore, you cannot restrict yourself to one or two languages. Again to mention that different jobs are done better in different languages.
R Language
This language was developed as a modern version of the S language developed in Bell Laboratories, the R language is combined with lexical extraction, which tends to provide the flexibility to produce statistical models. R is a really powerful language to start with machine learning since there are many specific GNU packages available. One can choose to use R to create powerful algorithms and, in addition, the R study has an easy statistical visualization of its algorithms. Although language is widely used in academic research and is gaining a really good recognition in the use of the industry more recently.
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Python
The Python language is one of the most flexible languages and can be used for several purposes. Python has gained great popularity based on this. Python contains special libraries for automatic learning, that is, scipy and numpy, which are excellent for linear algebra and for knowing the kernel’s automatic learning methods. The language is excellent to use when working with machine learning algorithms and has a relatively simple syntax. For beginners, this is the best language to use and to start.
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C language
The mother of all languages is definitely a great programming language to build your predictive algorithms. Developed at Bell Labs by Denise Ritchie (winner of the Turing Computer Scientist Award). This language is not child’s play and should be considered only when it has solid foundations of computer science and programming languages, however, once you have mastered the C language, there is nothing that can prevent you from developing your advanced algorithms. One does not need Ph.D but he knows thoroughly the concepts of computer programming. You can easily create your own regression analysis and simulation of time series, which would create strong machine learning algorithms.
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In conclusion, I would like to add that there are many other languages that you can use after reading the above. Once you dig deep you can explore functional languages like Haskell, Erlang, Julia and Scala, these tools need you to have a good knowledge of C first. As a beginner, you can start with Python and move to other languages once you have command of that. Machine learning is a process to build AI-enabled algorithms with which machines can automatically learn or produce codes through the analysis of the data given.
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