RAI Crash Course: Decision Trees, Random Forests, Boosting, KNN & SVM | Module 2, Ch 4

The machine learning toolbox, animated. The bias-variance trade-off and the 'dial' every model has, decision trees and Gini impurity (with a worked example), pruning, bagging and random forests, boosting, K-nearest neighbours and the curse of dimensionality, and support vector machines with C and kernels. From zero. ⏱ Chapters 00:00 Trees, forests, boosting, KNN and SVM 00:41 One idea behind every model 01:41 Animation: Decision trees 02:55 Example: Gini gain 03:56 Animation: Bagging and random forests 04:59 Animation: Boosting 05:58 Bagging vs boosting 06:45 Animation: K-nearest neighbours 07:45 Animation: Support vector machines 09:58 Recap 📚 Full course: see the "RAI Crash Course" playlist on this channel ▶ Next: Semi-Supervised & Reinforcement Learning | Module 2, Ch 5:    • RAI Crash Course: Semi-Supervised & Reinfo...   ⚠ Unofficial: this is an independent study guide. It is not affiliated with, endorsed by or sponsored by GARP. "RAI" refers to the Risk and AI certificate only to describe what the material helps you study. All explanations, examples and questions are original and written in our own words; always study from the official curriculum. 🤖 AI disclosure: the narration is an AI-generated (synthetic) voice, and the visuals are generated programmatically. Scripts are human-reviewed. No real exam questions are used or disclosed. #RAI #RiskAndAI #MachineLearning #AIExplained