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Julia is a relatively new programming language that has been gaining traction in the Machine Learning world. As it is getting more and more popular, I wanted to do this article to give you an in-depth overview of why and how to get started with Machine Learning in Julia. The main advantage of Julia over other Machine Learning languages is speed. Two main reasons for Julia’s speed advantage are, firstly, that it is a compiled language. Secondly, it has been designed for parallelism.

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The resources for Machine Learning in Julia are still relatively distributed over different packages. Julia being not (yet) as popular as other programming languages for Machine Learning, it can sometimes be a bit of work to find specific models. It can also be more effort to find (or write) certain basic data preparation functions that are easily available in Python and R. The good news is that there are initiatives to regroup Machine Learning models in larger libraries. At this point, there are two libraries that are seriously competing for becoming the go-to Machine Learning library in Julia: MLJ and Scikit Learn.

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Why Julia programming language stands apart from existing frontrunners?

Top tech giants like Amazon, Google Maps, Netflix, Siri, & more come under the hats of machine learning technology. With a plethora of data-driven prediction models & solutions, the innovation of Julia adds fuel in the fire in terms of speeding up the optimization process for increased ROI. ML is predicted to achieve excellent growth rate with a CAGR of 43% between the year 2019-24 in combination with Julialang.

Since its launch in 2012, Julia came out as a high-level general-purpose open-source programming language developed to perform high-end computations. If you’re new to this programming language then this brief introduction will definitely keep you engaged for the next few minutes. Julia stockpiles feature like multiple dispatch, built-in package, distributed computing, multiple dispatch, & other add-ons. Though it merges C, Python, MATLAB, Ruby, & R, it gained popularity within a shorter span of time, particularly for this reason.



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