MLAlgorithms

Why we included this project

MLAlgorithms is for people who want to read the code, not just call it. Where optimized libraries bury the logic under layers of performance tricks, these implementations stay close to the math: linear models, SVMs with kernels, random forests, k-means, PCA and naive Bayes are written in plain Python on numpy and scipy, with the inner workings left exposed. The repo works well alongside a textbook or course, letting you trace how gradient descent, EM for Gaussian mixtures, or backpropagation actually play out step by step. The honest trade-off is that this is a study resource, not production software. For anyone learning machine learning from the inside out, that is exactly the point.