In this module, you’ll be introduced to the fundamentals of machine learning (ML). You’ll learn the definition and principles of ML, and gain practical skills in calculating and comparing ML performance metrics. You’ll get a chance to understand how to construct ML classifiers and analyze their effectiveness across different algorithms. This module prepares you to apply ML techniques effectively in various domains, enhancing your ability to solve complex problems using data-driven approaches.

In this module, you’ll explore the significance of IAA in Machine Learning (ML) performance. You’ll learn to calculate IAA manually and implement Krippendorf’s Alpha using the software. You’ll gain insights into how IAA impacts the reliability of annotated data and its implications for ML model training. This module equips you with essential skills to ensure consistency and reliability in data annotation processes, crucial for effective ML applications.

In this module, you’ll be introduced to the concept and practical applications of crowdsourcing. You’ll get a chance to learn how crowdsourcing enhances problem-solving through collective efforts and explore real-world use cases. You’ll be able to establish your first Amazon Mechanical Turk (AMT) account and understand the platform's capabilities for executing crowdsourced tasks. You’ll get a chance to delve into crowdsourcing design principles to optimize task efficiency and reliability. This module prepares you to leverage crowdsourcing effectively for diverse applications, from data annotation to research experiments.

Platform" module focuses on leveraging Amazon Mechanical Turk (AMT) for crowdsourcing studies. Learn to design effective experiments using AMT, ensuring optimal task design and participant engagement. Collect data through AMT and perform initial analyses to derive meaningful insights from crowdsourced data. Understand the implications of AMT addiction and ethical considerations in platform-based research. This module equips you with practical skills to conduct reliable and insightful crowdsourcing studies using AMT.

This module explores the intersection of crowdsourcing and ML performance enhancement. Evaluate how Inter-Annotator Agreement (IAA) affects ML model reliability and accuracy. Explore case studies such as COVID test kit distribution and organ transplant matching to understand real-world applications. Learn to optimize ML performance through effective crowdsourcing design, ensuring data quality and reliability in machine learning applications.