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

4.8
stars
194 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.

JI

Jul 10, 2024

I like this course; it is very informative. I learned a lot of useful concepts, and I reinforced much of what I knew. I recommend this course, even if is just for fun.

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26 - 38 of 38 Reviews for Managing Machine Learning Projects

By Arunangsu s

•

Aug 13, 2023

Excellent . Organized in detail

By Vikram T

•

Aug 1, 2023

My ratings are 4.7 stars

By Ali A

•

Mar 26, 2022

Very insightful

By Jose A B

•

Jun 15, 2022

Excellent!

By 王亦凡

•

Nov 15, 2021

Great!

By mohammed k

•

Apr 16, 2024

good

By Shaik H

•

Apr 17, 2023

E

By M . k R

•

Oct 17, 2023

.

By Marnie C

•

May 6, 2024

Highly informative course. I'm grateful for this course because it made me think about a lot of things I think I would've missed were I to just try managing an ML project for the first time. It's clear that there are additional considerations in ML projects that aren't present in standard software product management. I would've like if they had provided some examples on how a deep learning project (like photo identification) might be different than a simpler model like regression from the product management perspective. The other improvement I think would've been more actual multiple-choice tests at the end of each chapter - the "discussion" tests are helpful to a degree for thinking through the information, but you don't get any feedback on them. I'd actually like something that tests whether or not I'm learning the material as well. But overall, for people who are genuinely dedicated to learning about ML projects, this was a helpful course.

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

By ABDOULAYE K B

•

Nov 4, 2024

TRES BIEN