Data Collection and Preparation

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Related lectures (32)

Page 1 of 4

Introduces applied machine learning concepts such as data collection, feature engineering, model selection, and performance evaluation metrics.

Machine Learning Basics

Introduces machine learning basics, including data collection, model evaluation, and feature normalization.

Applied Machine Learning: Features and Models

Explores data collection, feature selection, model building, and performance evaluation in machine learning, emphasizing feature engineering and model selection.

Research Methodology: Introduction

Covers research methodology, including journal production, anthropology, ethnography, survey methods, and digital studies.

Management of Fever in Children: Measurement and Examples

Explores the measurement and operationalization of outcomes in managing fever in children under five through examples and data collection approaches.

Digital Humanities Workflow

Explores the workflow in Digital Humanities, emphasizing data collection and visualization.

Google Analytics 4: Understand & set up GA4

Introduces Google Analytics 4 and guides on setting it up for WordPress websites, emphasizing key differences and event tracking.

Logistic Regression: Vegetation Prediction

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Machine Learning for Behavioral Data

Introduces a course on Machine Learning for Behavioral Data at EPFL, covering ML algorithms, data handling, and model evaluation.

Research Design: Intermediate Variables

Emphasizes specificity in defining variables and the importance of incorporating intermediate variables in educational studies.