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.
Über diesen Kurs
Kompetenzen, die Sie erwerben
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.
- 5 stars83,78 %
- 4 stars13,51 %
- 3 stars2,70 %
Top-Bewertungen von ARTIFICIAL INTELLIGENCE DATA FAIRNESS AND BIAS
Really great discussion of algorithms and how their designs make them susceptible to bias.
A relatively short and interesting course on data fairness and bias impacting AI models.
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!
An excellent reminder that the bias-variance trade-off is not the only trade-off machine learning specialists make.
Über den Spezialisierung 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.
Häufig gestellte Fragen
Wann erhalte ich Zugang zu den Vorträgen und Aufgaben?
Was bekomme ich, wenn ich diese Spezialisierung abonniere?
Ist finanzielle Unterstützung möglich?
Haben Sie weitere Fragen? Besuchen Sie das Learner Help Center.