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

Learn how to implement a Decision Tree from scratch in Python using just math and NumPy (no machine learning libraries like Scikit-Learn). I’ll start by explaining the intuition and core math concepts behind Decision Trees, including Entropy, Gini Impurity and Information Gain, and then walk through how to turn that into code. This is Episode 5 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/iris 💻 Full Code on GitHub: https://github.com/harryconnor/Machine-Learning-From-Scratch TIMESTAMPS: 00:00 - Introduction 00:23 - PART 1: Intuition & Math 05:21 - PART 2: Coding it up #python #machinelearning