Applied Machine Learning

This lecture covers the fundamentals of applied machine learning, focusing on learning from data. Topics include data collection, feature engineering, model selection, and performance evaluation metrics like precision, recall, and F1-score. The instructor emphasizes the importance of feature selection, model tuning, and the trade-off between bias and variance in machine learning models.