Machine Learning BasicsIntroduces the basics of machine learning, covering supervised and unsupervised learning, linear regression, and data understanding.
Data Mining: IntroductionCovers the challenges and opportunities of data mining, practical questions, algorithm components, and applications like shopping basket analysis.
Singular Value DecompositionExplores Singular Value Decomposition and its role in unsupervised learning and dimensionality reduction, emphasizing its properties and applications.
EW-MFA at a canton levelExplores EW-MFA at a canton level, covering data collection, import/export, emissions, waste management, and resources productivity.
Data Science EssentialsCovers the essentials of data science, including data handling, visualization, and analysis, emphasizing practical skills and active engagement.
