Feature Scaling in Machine Learning | Normalization vs Standardization | Industry ML Thinking скачать в хорошем качестве

Feature Scaling in Machine Learning | Normalization vs Standardization | Industry ML Thinking

This session focused on one of the most critical preprocessing steps in Machine Learning — Feature Scaling — and the concept that ML algorithms operate purely on numerical magnitude without real-world context. The discussion explained how features with larger values can dominate smaller ones mathematically, leading to biased and misleading model behavior if scaling is not applied. Two primary industry-standard scaling techniques were covered: • Normalization (Min-Max Scaling) to transform values into a fixed range such as 0 to 1 • Standardization (Z-score Scaling) to center data around mean 0 with standard deviation 1, making it more robust to outliers and commonly preferred in production environments The session also included an industry-focused ML problem formulation assignment, where a real business scenario was analyzed to justify the need for Machine Learning, define inputs, outputs, and labels, and evaluate risks such as bias, privacy, and incorrect predictions—without writing code. This strengthened structured analytical thinking and real-world ML reasoning beyond theory. Overall, the learning emphasized magnitude bias awareness, faster model convergence through scaling, correct industrial preprocessing practices, and the ability to translate business problems into structured Machine Learning workflows. #MachineLearning #FeatureScaling #Normalization #Standardization #AI #DataScience #MLEngineering