Line Estimation

Before I start my post, I want to inform you that there might be an unclear succession between the posts I publish. I am doing my best to keep them in order, but as a general rule in life, nothing is guaranteed! I mean there are so many things that I want to write about, and every time I want to write, something comes to my mind which is not necessarily directly related to my previous posts. So please bear with me on this. Continue reading

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Similarity and Distance Functions

Let’s go back to our posts on distance functions. The initial idea was to calculate the distance between two points or between one point and a set of points in a 2d space. Within the discussion, we often stated that one point is more “similar” to the set, than the other point. So, maybe what we are actually trying to calculate is the similarity between two entities (either being a point or a set). The problem is that similarity is a subjective matter, so we need an objective measure, and that’s why we have chosen distance. Continue reading

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

About

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.