Applied Machine LearningIntroduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
Machine Learning and PrivacyDelves into machine learning's impact on privacy, discussing attacks, vulnerabilities, and ethical considerations in data usage.
Machine Learning BasicsIntroduces machine learning basics, including data collection, model evaluation, and feature normalization.
Subdivision ApproachExplores the subdivision approach to manage multifunctionality in life cycle analysis.
Machine Learning FundamentalsCovers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
