Show HN: I built 10 ML algos from scratch because fit() predict() are not enough (github.com)

A new coding resource titled "Machine Learning From Scratch" has been launched, aiming to demystify the inner workings of core machine learning algorithms by constructing them from the ground up. This initiative offers a structured 5-stage framework—Intuition, Formalization, Implementation, Testing, and Tips—that enables individuals with basic Python skills and high-school math to grasp complex ML concepts comprehensively. It covers ten fundamental algorithms including Linear Regression, Logistic Regression, and Neural Networks, allowing learners to validate their implementations against industry-standard libraries. This project is significant for the AI/ML community as it promotes a deeper understanding of machine learning beyond black-box methods, empowering practitioners to develop a clearer mental model of algorithms and their applications. The resource emphasizes the importance of data preparation, optimization techniques, and practical issues like handling missing data and feature scaling. By providing both theoretical insights and hands-on coding experience, the initiative helps users not only learn how to build models but also how to approach real-world problems systematically, fostering a more robust understanding of the machine learning landscape.