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Learner Reviews & Feedback for Structuring Machine Learning Projects by DeepLearning.AI

4.8
stars
49,903 ratings

About the Course

In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the "industry experience" that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI....

Top reviews

MG

Mar 30, 2020

It is very nice to have a very experienced deep learning practitioner showing you the "magic" of making DNN works. That is usually passed from Professor to graduate student, but is available here now.

AM

Nov 22, 2017

I learned so many things in this module. I learned that how to do error analysys and different kind of the learning techniques. Thanks Professor Andrew Ng to provide such a valuable and updated stuff.

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5176 - 5200 of 5,721 Reviews for Structuring Machine Learning Projects

By Diego S

•

Feb 18, 2020

I miss notebooks for practice, besides questionnaires

By Xinghua J

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Sep 6, 2019

If there is some coding practice, it would be better

By Pranjal V

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Jul 11, 2020

Very well explained but needs more reading material.

By Hee s K

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Apr 18, 2018

It is an unique lecture providing empirical advises.

By Pablo L

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Oct 30, 2017

Great set of guidelines. Mostly theoretical, though.

By C. G F

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Oct 22, 2017

Concrete reminders of important and practical points

By Ktawut T

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Oct 10, 2017

Very useful materials for leading a ML research team

By Awalin S

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Sep 29, 2017

interesting insights about real world implementation

By Yu L

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Apr 3, 2020

would like to have more excercise related to coding

By Mage K

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Mar 7, 2018

Would've liked to have some programming assignments

By Carlisle

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Aug 20, 2017

Introduced a lot on engineering project experiences

By Marcelo A H

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May 29, 2020

Very interesting topics were shown in this course.

By William L

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Apr 17, 2020

Very useful knowledge that is not commonly taught.

By Alvaro G d P

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Nov 27, 2017

Interesting but perhaps we could have gone deeper.

By John H

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Aug 26, 2017

Is the flight simulator hw going to be added soon?

By Pat B

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Dec 8, 2019

Great course. I liked the compact, 2-week format.

By liu c

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Mar 17, 2018

A little bit abstract. But still very inspiring!

By Florian M

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Aug 24, 2017

Very interesting tools and ideas for applied ML.

By Nicholas N S

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Apr 28, 2021

There is so much noise in the explanation voice

By Jason G

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Nov 24, 2018

Not as strong as the other 4 of 5 of the series

By Mark

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Oct 12, 2018

Great course. Needs deeper practical examples.

By Francis J

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Feb 25, 2018

A lot of insights rather than technical details

By Lukáš L

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Jan 7, 2018

Coding exercises would be great in this course.

By Mares B

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Nov 17, 2020

A little short, maybe more hands on exercises?

By Ed G

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Nov 8, 2020

Concise course with some interesting concepts.