Variance or Standard Deviation

I just found out that I made a mistake in my previous post (it is fixed now). There, I argued that in order to calculate the distance between a point and a set (which has unequal variances along different dimensions), we can use the following formula:

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The Mighty Distance Function – Part 1

I’ll start my posts with the distance function, as I believe a complete understanding of it, provides the basic idea behind most of the ML algorithms. Some of the approaches which come next are closely related to some known algorithms in ML, but I am not going to name any of them, as I believe that a complete understanding of the intuition behind any of them is more important than their names. Continue reading

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Machine Learning (ML) should not be a hard read. That is my slogan for this page. With all the respect I have for all the great books out there on ML, I believe the intuition behind most of the ML algorithms can be explained more easily. And that’s what I am trying to do here. I will start with the distance function, I will go through different ML algorithms and try to explain them, do experiments with them or anything which help me to understand them better. Join my journey if you are also interested in ML, and I hope we can help each other learn more.