In this course, we will explore fundamental issues of fairness and bias in machine learning. As predictive models begin making important decisions, from college admission to loan decisions, it becomes paramount to keep models from making unfair predictions. From human bias to dataset awareness, we will explore many aspects of building more ethical models.
Este curso forma parte de Programa especializado: Ethics in the Age of AI
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Acerca de este Curso
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Curso 2 de 4 en
Nivel principiante
Aprox. 6 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish), Japonés
Habilidades que obtendrás
- machine learning fairness
- Ethics
- data bias
Fechas lÃmite flexibles
Restablece las fechas lÃmite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100Â % en lÃnea
Comienza de inmediato y aprende a tu propio ritmo.
Curso 2 de 4 en
Nivel principiante
Aprox. 6 horas para completar
Inglés (English)
SubtÃtulos: Francés (French), Portugués (europeo), Ruso (Russian), Inglés (English), Español (Spanish), Japonés
ofrecido por
LearnQuest
LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.
Programa - Qué aprenderás en este curso
2 horas para completar
Fairness and protections in machine learning
Welcome to the course! In week one, we will be discussing what fairness means in the context of machine learning and what true parity means in different scenarios
2 horas para completar
5 videos (Total 19 minutos), 2 lecturas, 3 cuestionarios
2 horas para completar
Building fair models: theory and practice
This week we will take action against unfairness. Now that we have an understanding of fairness issues, how do we build models that do not violate them?
2 horas para completar
5 videos (Total 21 minutos), 2 lecturas, 3 cuestionarios
2 horas para completar
Human factors: minimizing bias in data
This week, we will tackle the human biases that enter the data collection and attribute selection processes. The goal? Removing bias before the model is built
2 horas para completar
5 videos (Total 33 minutos), 2 lecturas, 3 cuestionarios
Reseñas
- 5 stars83,33Â %
- 4 stars13,88Â %
- 3 stars2,77Â %
Principales reseñas sobre ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
por AV14 de ene. de 2021
Extraodinary course! I've learnt so much! The classes are very informative and dynamic. Didn't feel like studying but rather entertaining myself with hight quality content! Thank you so much!
por CM2 de oct. de 2021
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
por SY30 de mar. de 2021
A relatively short and interesting course on data fairness and bias impacting AI models.
por RU19 de abr. de 2022
Really great discussion of algorithms and how their designs make them susceptible to bias.
Acerca de Programa especializado: Ethics in the Age of AI
As machine learning models begin making important decisions based on massive datasets, we need to be aware of their limitations. In this specialization, we will explore the rise of algorithms, fundamental issues of fairness and bias in machine learning, and basic concepts involved in security and privacy of machine learning projects. We'll finish with a study of 3 projects that will allow you to put your new skills into action.
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