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Learner Reviews & Feedback for Managing Machine Learning Projects by Duke University

4.8
stars
141 ratings

About the Course

This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems. Participants will learn about the data science process and how to apply the process to organize ML efforts, as well as the key considerations and decisions in designing ML systems. At the conclusion of this course, you should be able to: 1) Identify opportunities to apply ML to solve problems for users 2) Apply the data science process to organize ML projects 3) Evaluate the key technology decisions to make in ML system design 4) Lead ML projects from ideation through production using best practices...

Top reviews

AS

Aug 23, 2023

I really enjoyed this course. I already work in the area of AI but it was very useful to have someone explain key AI terms in a lay way. Highly recommended! The instructor was engaging and clear.

MR

Dec 29, 2022

Very important course for anyone interested in understanding the process involved in managing AI projects. Strongly recommended.

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26 - 31 of 31 Reviews for Managing Machine Learning Projects

By mohammed k

•

Apr 16, 2024

good

By Shaik H

•

Apr 17, 2023

E

By M . k R

•

Oct 17, 2023

.

By Ravi G

•

Sep 4, 2023

The peer rating for the final project is interesting, if someone who does not get what is being asked for the final project is going to rate my final project. Saw some interesting examples.

By RENE J E

•

Mar 28, 2024

The course provides valuable tools for managing projects in this field. I am confident that I will use these methodologies in future projects.

By Eslam E

•

Apr 4, 2024

It is useful, practical and has helped me complete many tasks