RAI Crash Course: Preparing Data, Garbage In, Garbage Out | Module 2, Ch 1

Module 2 starts with the data. Data types and what each allows, training, validation and test splits (and data leakage), scaling and log transforms, missing values and outliers with a box plot, turning categories into numbers and the dummy variable trap, and choosing features: selection vs extraction (PCA). Animated, from zero. ⏱ Chapters 00:00 Preparing data: garbage in, garbage out 00:45 A useful lens for every tool in Module 2 01:41 Training, validation and test data 02:51 Scaling vs transformation 03:30 When scaling matters, and when it bites 04:08 Missing values 05:34 Turning categories into numbers 06:28 Feature selection vs feature extraction 08:04 Recap 📚 Full course: see the "RAI Crash Course" playlist on this channel ▶ Next: Clustering, K-Means, Hierarchical & DBSCAN | Module 2, Ch 2 ⚠ 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