Applied Machine Learning: Features and Models

This lecture covers the importance of data collection, feature selection, and model building in machine learning. It discusses the types of features, feature engineering, discretization, and feature selection methods. The instructor emphasizes the significance of feature normalization, standardization, and scaling for model performance evaluation. The lecture also delves into precision, recall, F-score, ROC curves, and bias-variance trade-off in model selection. It concludes with insights on the impact of data size and model complexity on model performance.