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

4.8
stars
50,134 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

PD

Feb 24, 2020

Generally, the course is great. This is a short course and could be combined with other courses in this series. Also, some knowledge such as data splitting has been introduced in the courses before.

EM

Jul 7, 2020

I think this is the best way of understanding the models we build and train. Now I can understand where are the errors are coming from and how to focus and choose an error rate problem to solve.

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5201 - 5225 of 5,748 Reviews for Structuring Machine Learning Projects

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.

By Tulip T

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Jul 23, 2019

Quite helpful when you start a new ML project.

By S V R

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Nov 4, 2018

The session were simple, could be more complex

By Caique D S C

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Jul 30, 2018

very good course, could be less massive though

By Ivan V

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Dec 11, 2019

I want a program exercise like in 1-2 courses

By Dionysios S

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Nov 30, 2018

I would like to see more practice assessments

By Luis E R

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Jul 31, 2019

Very useful concepts that few people address

By Jun P

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Apr 22, 2018

Kind of boring than the cnn and rnn class ..