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Beginners to machine learning are often confused by the plethora of algorithms and techniques being taught in subjects like statistical learning, data mining, artificial intelligence, soft computing, and data science. It’s natural to wonder how these subjects are different from one another and which is the best for solving real-world problems. There is substantial overlap in these subjects and it's hard to draw a clear Venn diagram explaining the differences. Primarily, the foundation for these subjects is derived from probability and statistics. However, many statisticians probably won't agree with machine learning giving life to statistics, giving rise to the never-ending chicken and egg conundrum kind of discussions. Fundamentally, without spending much effort in understanding the pros and cons of this discussion, it’s wise to believe that the power of statistics needed a pipeline to flow across different industries with some challenging problems to be solved and machine learning simply established that high-speed and frictionless pipeline. The other subjects that evolved from statistics and machine learning are simply trying to broaden the scope of these two subjects and putting it into a bigger banner.
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