What Is a Machine Learning Engineer? (+ How to Get Started)
February 3, 2025
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This course is part of Ethics in the Age of AI Specialization
Instructor: Brent Summers
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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.
In Module 1, we are going to discuss what true anonymity and privacy mean in machine learning
5 videos2 readings3 assignments2 discussion prompts
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
4 videos2 readings3 assignments1 discussion prompt
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
5 videos1 reading3 assignments1 discussion prompt
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Course
Northeastern University
Course
LearnQuest
Specialization
Google Cloud
Course
83 reviews
74.69%
19.27%
4.81%
1.20%
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Reviewed on Apr 2, 2021
A relatively short but interesting course relating to privacy concerns around AI and ways to manage/improve models to address these concerns.
Reviewed on Dec 22, 2020
This was a very interesting eye opening Course of the Future, thank you.
Reviewed on Feb 12, 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
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