courseraПрограммирование

Training AI with Humans

Курс от Johns Hopkins University

Средний≈ 22.8 чАнглийский

О курсе

In the course "Training AI with Humans", you'll delve into the intersection of machine learning and human collaboration, exploring how to enhance AI performance through effective data annotation and crowdsourcing. You’ll gain a comprehensive understanding of machine learning principles and performance metrics while developing practical skills in using platforms like Amazon Mechanical Turk (AMT) for crowdsourced tasks. This unique approach combines theoretical knowledge with hands-on experience, allowing you to implement Inter-Annotator Agreement (IAA) techniques to ensure high-quality annotated data. By completing this course, you will be well-equipped to design and conduct impactful crowdsourcing studies, improving AI models in real-world applications such as healthcare and research. Whether you're looking to enhance your skills in machine learning, optimize data collection processes, or understand the ethical implications of crowdsourcing, this course offers invaluable insights and tools.

Навыки, которые вы освоите

Data Quality Data Collection Model Evaluation Artificial Intelligence and Machine Learning (AI/ML)Data Validation Statistical Analysis Machine Learning Software Model Training Research Design Experimentation Data Capture Machine Learning Algorithms Applied Machine Learning Machine Learning Methods Model Optimization Amazon Web Services Data Ethics Classification Algorithms Machine Learning

Программа курса

6 модулей · 49 учебных материалов

01Course Introduction2 материалов

Course OverviewЧтение Instructor Biography - Dr. Ian McCulloh PLUGIN

02Machine Learning11 материалов

Introduction to Machine Learning

Machine LearningВидео

Учитесь у экспертов

Ian McCulloh

Преподаватель курса

Training AI with Humans

О курсе

Навыки, которые вы освоите

Программа курса

Introduction to Machine Learning

Учитесь у экспертов

Ian McCulloh

Evaluating and Constructing ML Classifiers

Module-end Assessments

Understanding Inter-Annotator Agreement (IAA)

Calculating and Implementing IAA

Module-end Assessments

Introduction to Crowdsourcing

Setting Up and Designing Crowdsourcing Tasks

Module-end Assessments

Designing Crowdsourcing Studies with AMT

Collecting and Analyzing AMT Data

Module-end Assessments

Impact of Inter-Annotator Agreement on ML Performance

Designing Effective Crowdsourcing for ML Improvement

Module-end Assessments