Machine-Learning

Why we included this project

Anyone learning classical machine learning from scratch will find a complete walkthrough here. Each algorithm, from k-nearest neighbors and decision trees to SVM and naive Bayes, comes with working Python 3 code, a training dataset, and a detailed write-up that explains the theory as you go. The small runnable examples, like a dating-site match classifier or a handwritten-digit recognizer, are easy to step through line by line in a notebook. Because the chapters build into a coherent course rather than a pile of loose scripts, the repo works well for self-study, reviewing for an exam, or brushing up on interview topics. Just treat it as a teaching resource: the code shows how each method works, not how to wire it into a production application.