Applied Machine LearningIntroduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.
Data Collection and PreparationDiscusses the significance of data collection and preparation for classification, including labeling challenges and crowdsourcing methods.
Machine Learning BasicsIntroduces machine learning basics, including data collection, model evaluation, and feature normalization.
Data Sources: Surveys and TechniquesExplores quantitative data sources, survey methods, and qualitative research techniques for comprehensive data collection in transportation planning.
