Machine Learning From Scratch - Naive Bayes FROM SCRATCH in Python (no scikit-learn, just math)

Learn how to implement a Naive Bayes Classifier from scratch in Python using just math and NumPy (no machine learning libraries like Scikit-Learn). I’ll start by explaining the core math concepts behind Naive Bayes, including the Bayes Theorem and the Gaussian Distribution to deal with continuous features, and then walk through how to turn that into code. This is Episode 4 of my Machine Learning From Scratch series, where I’m building ML algorithms from the ground up, step-by-step to truly understand how they work under the hood. 📺 Watch the Playlist Here: https://www.youtube.com/playlist?list=PLh6JMkwECi5HXVJ58ue58jJvL599NFYja 📊 Kaggle Dataset: https://www.kaggle.com/datasets/uciml/breast-cancer-wisconsin-data 💻 Full Code on GitHub: https://github.com/harryconnor/Machine-Learning-From-Scratch TIMESTAMPS: 00:00 - Introduction 00:23 - PART 1: The Math 05:00 - PART 2: Coding it up #machinelearning #python