This post aims to dissect the steps in performing logistic regression on a single sample. This also covers some explanation on cross-entropy loss.
How does kNN work? How does it find nearest neighbors? How does it work in Python?
In this post, I’ll discuss what a hyperplane is, what SVM does, and the basics of how to get the optimal hyperplane in both linear and non-linear data.
I’m aiming to write about a machine learning algorithm or concept every few weeks. This page contains a collection of my ML guides/lessons that hopefully someone might find useful.
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