Chevron Left
Back to Structuring Machine Learning Projects

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

4.8
stars
49,925 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

TG

Dec 1, 2020

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

ED

Aug 22, 2020

Excellent start for digging into topics that are not taught nowhere else. The author books 'Machine Learning Yearning' is a great next read that goes deeper in some of the aspects, really recommended.

Filter by:

4851 - 4875 of 5,724 Reviews for Structuring Machine Learning Projects

By Sujay K

Mar 25, 2018

The course would have been more interesting if we had some programming assignments. Hands on experience into some of these cases really help.

By Daniel M

Jan 14, 2018

Unique course in the sense that teaches important topics that are rarely seen in the literature and are fundamental in designing AI projects.

By Hagay G

Apr 9, 2019

Had some pretty great info for junior Project Managers, for some reason, it's also hiding some extremely important info about end-to-end DL.

By Jun-Hoe L

Feb 19, 2022

Course is a little short, only 2 weeks and a quiz. I feel there could have been another week added, with another interesting case study ,

By Mohamed M H M A

Apr 22, 2018

Some of the videos weren't of good quality. Also, I was expecting doing a real project not to make decisions based on different scenarios.

By ARPIT J

Aug 29, 2022

This is an important course for practical purpose. It tells us about the methods to use for improving your model by doing error analysis.

By Nikolai K

Oct 3, 2017

Good course overall, would have liked to have the in-depth programming assignments though, those really made the other courses stand out.

By Shashank S S

Jul 8, 2019

Learned various ways to structure ML projects in industry.

It would have been great to have few programming assignments included as well.

By Leonid

Oct 5, 2017

Some tips are very useful for practitioners but the same information is repeated over and over again that makes the course quite boring.

By aman a c

May 18, 2020

A small course with very effective tips and tricks to figure out how to start and proceed further while building a project effectively.

By 김진수

Feb 25, 2019

I think this lecture is very useful when we make our own ML system.

Also, it has many examples about errors we can usually meet in real.

By Deleted A

Feb 25, 2018

Useful, practical material. I probably underappreciate the importance of someone (especially of Dr. Ng's stature) covering this for us.

By Bill T

Feb 24, 2018

Very practical lessons in this module that should make you and your team more efficient in implementing deep learning on real problems.

By Edward M

Dec 24, 2019

another great Andrew Ng course. This one gives practical insights in how to go about making your deep neural networks perform better.

By Mohammad H

Dec 17, 2019

I really found the pilot training quizzes are great and very helpful, but some questions one can debate if has the right answer or not

By Riley

Apr 8, 2019

Quizzes could be refined since some of the questions are really confusing & need weird pre-requisite knowledge about human physiology.

By Ioannis “ K

Aug 14, 2018

It was an interesting course for sure, but it was a bit stretched and the notions explained could be compressed in a much shorter one.

By John E M

Mar 31, 2018

I appreciate the review and hints on structuring ML projects. Just seemed a little lacking on the meat and potatoes of real practice.

By Saurabh D

Aug 26, 2020

Now I know what is Machine learning and its parts eg deep learning. The curse cleared the basic structure for machine learning to me.

By JEREMY S

Jun 7, 2020

Interesting to understand how to manage a problem during a ML project, really good trick and tip! Thanks Andrew and deep learning.ai!

By Alhasan A

Jun 1, 2019

It would be more useful to give explanation why an answer is correct and others are wrong, such details enhance our learning so much.

By aditya g

Feb 21, 2018

Machine Learning Simulator & course contents well prepares you to how a machine learning project should be structured and approached

By Chee H H

Nov 24, 2017

Probably the least exciting of the five. This is a short course on how to approach machine learning projects, as the title suggests.

By Priyanka T

Oct 22, 2017

I thought this course was great content wise, but needs to improve on the errata in the content (repeated video sections), and quiz.

By 李炳男

Feb 1, 2018

I think it should be useful but since I haven't got many practical experience, the course seems a little bit hard to catch up with.