Chevron Left
Back to Structuring Machine Learning Projects

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

4.8
stars
47,532 ratings
5,451 reviews

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

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.

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.

Filter by:

4451 - 4475 of 5,412 Reviews for Structuring Machine Learning Projects

By Mark M

Nov 21, 2017

This course is at all an important part during the learning journey. The only reason why I not rate full 5 stars that the recommendation ramen little bit on high level and do not address typical frame conditions in real world projects.

By Oliver M

Aug 16, 2017

Lots of practical stuff about training models. But you should try building a few models before doing the course. Otherwise, you may not fully appreciate how much time can be wasted unless you use Andrew's clear and logical approaches.

By Wei Z

Oct 22, 2017

Lots of interesting and useful idea. Unfortunately the editing is poor and Professor Andrew Ng has gone a little bit repetitive in his talking in this course only. The two previous courses were great but this one is kind of dragging.

By Saad T

Sep 6, 2017

I am a big fan of the jupyter notebook assignments. I can understand that it could be hard to build python assignments for this course, but not impossible I think (maybe around error analysis, impact of artificial data synthesis...)

By S A

Jun 11, 2018

The content of the course lecture is great. The teaching is great. One problem is the quality of subtitles. The black background does not allow to see what is shown behind. It would be better if the background would be transparent.

By Sarah W

Mar 21, 2018

Great material! Some of the videos went a bit long, and I think the point could have been made in much less time. However, overall this series has been great and I still got some very valuable info out of this course, so I'm happy.

By Michael A

Dec 7, 2017

The course was very well structured and Andrews explanations was wonderful as usual. The only thing I was missing was more practical hands-on in the form of a programming exercise or two to really demonstrates the different ideas.

By Hanling S

Dec 8, 2020

Andrew really provided great content, but the edition of this course is not as good as the first two, sometimes you will hear some repetitive sentences or a long pause. Hope they can upgrade this part, all the others are terrific.

By Cheng J

Sep 20, 2020

This course give a lot of useful practical advices on training a machine learning/deep learning models. However, some of the advices are rather subjective and experience based, and some of the homework answers are quite debatable.

By ashwin m

Jul 1, 2019

this course provided very interesting insight into missing , incorrectly classified labels and also how existing models can influence the training of a new model which is on similar lines as the task the existing models performed

By Jithin V

Jan 3, 2021

Great course for machine learning strategies in deep learning.

Several concepts which aren't discussed in other courses have mentioned .

Especially the new way of splitting the datasets, transfer learning, multitask learning etc.

By Silvério M P

Sep 6, 2018

Looking at practical examples is an enormous help and some concepts i learned here will undoubtedly be useful in the future, i just think there should be more of it. It's just really short both in duration as well as content

By Vignesh S

May 28, 2019

It was really good to know how to structure and tune the nn so as to achieve a better model. But, I felt that it had too much theory in it that is hard to remember every time a model is to be designed. Overall, it was good.

By Rahul P

Aug 24, 2020

One of the quick and great course for individual and team for understanding how to handle and structure the machine learning project. how to improve accuracy and handle error such a wonderful course made by deeplearning.ai

By chandrashekar r

Sep 18, 2017

I rate the course high. Unfortunately many of questions (posed in the forum) have not been answered.

Her are some suggestions:

Have quiz after every lecture. That will firm up the concepts.

Give lesser help in assignments.

By Gustavo S d S

Jan 4, 2018

Gives a sense about improving the performance of Deep Neural Networks, with error/bias/variance/data mismatch analysis. However, there is a lack of hands-on exercises, not having a programming assignment, only quizzes.

By Michael F

Oct 19, 2018

Lots of useful tips and tricks in this course. I feel that the videos could have been a bit shorter, and it would have been nice to have some programming assignments. Overall the course was extremely useful, however.

By Grant G

Dec 3, 2017

A pleasant diversion into practical considerations of project design. However the lack of programming assignments and the somewhat vague and fiddly quizzes make this a less satisfying course than it could have been.

By Jeffrey D

Mar 31, 2020

This was a good overview of the concepts I have already learned. It was a good refresher on progress and changes in training best practices. There are a few flawed questions in both quizzes that need to be fixed.

By gjycoursera

Jun 27, 2020

from my perspective, maybe, it would be better if this course is the end course of the specialization. the contents are greate. I would like to suggest others to put this course in the end of the specialization.

By Othman B

Jan 2, 2018

Very interesting, but too short. The aim of the course is to provide a good overview of the different situations occuring in a project, but there is more questions arising. Experience will come with training.

By Antti R

Nov 3, 2019

nice to follow, but I would have liked it there would have been more variance. e.g. quizzes breaking the videos. I'm basically comparing this experiment with the other courses made by Andrew/deeplearning.ai

By Samuel C

Oct 14, 2018

A useful few hours of videos. I found the questions quite useful, but overall feel this project would have been better off being spread across other weeks, as it doesnt work so well as a stand-alone course.

By Sardhendu M

Oct 21, 2017

Very practical. programing assignment using the concepts would help to solidify the concepts. I would really appreciate programming assignments on Transfer learning since a lot of industries practices it.

By Wiebe V

Aug 30, 2018

Clear course, it would have been helpful to add notebooks to the course to have a more realistic feeling of the problems. This would make it also more clear how the dev set influences the training phase.