In this course, we will explore fundamental concepts involved in security and privacy of machine learning projects. Diving into the ethics behind these decisions, we will explore how to protect users from privacy violations while creating useful predictive models. We will also ask big questions about how businesses implement algorithms and how that affects user privacy and transparency now and in the future.
Este curso faz parte do Programa de cursos integrados Ethics in the Age of AI
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Informações sobre o curso
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Prazos flexíveis
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Certificados compartilháveis
Tenha o certificado após a conclusão
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Curso 3 de 4 no
Nível intermediário
Aprox. 6 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
Habilidades que você terá
- Ethics
- Machine Learning
- Ethics Of Artificial Intelligence
- security
- Privacy
Prazos flexíveis
Redefinir os prazos de acordo com sua programação.
Certificados compartilháveis
Tenha o certificado após a conclusão
100% on-line
Comece imediatamente e aprenda em seu próprio cronograma.
Curso 3 de 4 no
Nível intermediário
Aprox. 6 horas para completar
Inglês
Legendas: Francês, Portuguese (European), Russo, Inglês, Espanhol
oferecido 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 - O que você aprenderá com este curso
2 horas para concluir
Privacy and convenience vs big data
In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
2 horas para concluir
5 vídeos (Total 19 mín.), 2 leituras, 3 testes
2 horas para concluir
Protecting Privacy: Theories and Methods
In Module 2, we are going to take a deeper look at dataset security. We will also look into methods to add privacy to existing and new datasets to protect those individuals in them
2 horas para concluir
4 vídeos (Total 16 mín.), 2 leituras, 3 testes
2 horas para concluir
Building Transparent Models
In Module 3, we will discuss putting ethical, private models into practice. We will explore the explainable AI movement as well as tradeoffs for the teams putting together these algorithms
2 horas para concluir
5 vídeos (Total 20 mín.), 1 leitura, 3 testes
Avaliações
- 5 stars68,88%
- 4 stars26,66%
- 3 stars4,44%
Principais avaliações do ARTIFICIAL INTELLIGENCE PRIVACY AND CONVENIENCE
por YL22 de mai de 2021
The concepts were easier to grasp and a nice introduction into the complexities around algorithmic models and building ethical practices from the outset.
por CA12 de fev de 2021
A good course on balancing between privacy and aggregate results. It tells how anonymization should be done. It did not cover enough the correlations between privacy and accuracy though
por CM2 de out de 2021
This course provides practical steps to protect privacy.
por SY2 de abr de 2021
A relatively short but interesting course relating to privacy concerns around AI and ways to manage/improve models to address these concerns.
Sobre Programa de cursos integrados 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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