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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição

47,357 classificações
5,435 avaliações

Sobre o curso

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....

Melhores avaliações

1 de Dez de 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.

30 de Mar de 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.

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4376 — 4400 de 5,397 Avaliações para o Structuring Machine Learning Projects

por Fritz L

23 de Set de 2018

I liked the course but it contained quite a few glitches which could be easily removed to improve the overall experience. E.g., once Prof. Ng makes a long pause and says "test". Sometimes the same ending is placed twice or in the final "Heros of Deeplearning" video Prof. Ng seems to ask the same question twice.

por Jingchen F

7 de Jul de 2018

this course is pretty different from other courses in this specialization. It gives high-level knowledge of machine learning instead of implementation details. The course content is useful but it seems a little boring to me because I can't do any fancy, real machine learning projects as exercises in this course

por Edgar L V

5 de Ago de 2019

The quizzes were actually a great idea. The content is definitely useful, as I've had similar difficulties in my company. I felt the videos took much more time than they should, though. A lot of the content could have been resumed in shorter videos. It was the first time I actually had to accelerate the speed.

por Sebastiaan v E

17 de Nov de 2017

Good materials.

This course was really short though. It seems to be a bit artificial to make a "specialization" out of these courses, where they could easily also fit into 1 longer course. Fortunately the dates you can start the courses are flexible enough that you don't need to wait (too long) between courses.

por Andrew W

26 de Jan de 2021

Excellent information about how to diagnose errors during machine learning and complete projects well. I would have liked a small coding aspect to see how certain concepts (eg. train-dev set, transfer learning etc. are implemented), even some very basic examples would have helped. Overall still a great course

por Rosmiyana

13 de Abr de 2020

Good course to get started with Machine Learning, the introduction video could have used simpler languages though as many of the jargon might not be familiar to newbies (therefore scare us off!!) and they are really not necessary prerequisites to the course. I enjoyed the quizzes as they are real and useful.

por Alexandru S

8 de Set de 2017

Very interesting material covered - not too many courses have this kind of information.

A little too short and very no practical assignments (only quizes). It would be very useful (although I agree quite time consuming to prepare) to have some programming assignments that deal with the topics in the curse.

por Jasper v H

3 de Abr de 2020

Good general introduction to analyzing errors and avoiding common mistakes in machine learning projects and some info on transfer learning and multitask learning. Could've used references for further reading. It should emphasize exploratory data analysis and an ethics review as the start of any project.

por Ernst H

9 de Jul de 2019

4 stars for a very good course that should be improved. Course is still good, but it is not as polished as the first courses in this series. I rated those with 4 stars, too. There are mistakes in the quiz names, grammatical errors in quiz questions, etc. Never-the-less, it is the best of its kind.

por Karim A S

17 de Mar de 2021

Good course helped a lot to gain insights into the problems of machine learning but I would say more exercises are needed even if these guided exercises are good.

maybe add an exercise where you can simulate a fake NN and get the result and then choose what to do to get a feel of what you should do.

por MIchael

19 de Nov de 2017

Interesting insights.

The insights could be visually structured a bit better so that I can also check them after the course as a reminder.

Often recommendations like if then could be put in processes or cheat sheets

overall: very valuable course regarding the insights and encouraging style of Andrew Ng


22 de Jun de 2020

Well structured course, Andrew always never lets down your expectation, the explanations were very clean with the best appropriate examples to suit the explanation. Being more of theoretical, the task of giving us the correct intuition is really well handled by the way the lectures are structured.

por Kharuk I

20 de Jul de 2020

Instead of clearly and concisely formulating some of the ideas (like F1 formula for the metric), they are discussed as if they were hard to understand. This makes the understanding harder and is rather annoying. The assignments are useful - they make sure you understood the material the material.

por Burag C

10 de Abr de 2018

This was a good intuition course. I learned a lot and loved the content. However, I am afraid the information here needs to be repeated many times to make it a habit (as part of programming exercises). That's why I am giving it a 4-star. I feel like this could have been part of the last course.

por Kevin C

22 de Dez de 2019

Great overall. However, a major thing that is missing is the different between val set and dev set, and about the recent trend to perform K-Fold CV on the training set to get the val set. Maybe still need a separate dev set because of the different distribution if it comes a separate soure?

por Anthony

15 de Nov de 2017

Wish there were more projects / assignments to exercise concepts taught. Like in the first 2 courses of the specialization.

Maybe even blending videos with a broader Jupyter notebook would be better. The videos are great, but paired with practical application its much more likely to stick.

por Prashant S

2 de Mar de 2018

-This course have two quizzes and no programming assignment.

-This course gives a very good advice on how we can improve Algorithm performance.

-Best way to split data into Train/dev/test.

-Quizzes statement can be made more precise and clear but stil the scenario in the quiz was good.

por Timo K

20 de Dez de 2017

Very good course, but in contrast to the other courses the practical exercises are missing. I would like to see some transfer learning and (non-)end-to-end learning approaches, where the student has to examine how bad/good end-to-end performs in contrast to a multi-step approaches.

por Dmytro S

24 de Set de 2017

The content of this course is quite unique. Thus it makes it much more interesting and important.

Thank's a lot for this tips!

However it would be nicer if there is some videos practical assignments about tech aspects of implementations of "transfer learning" and "multitask learning"

por Christopher M

29 de Jul de 2020

A very nuanced course, which I see myself coming back to, gaining more insight and appreciation for it over time. Some of the quiz questions where quite difficult to answer as they were open to interpretation. I think Prof. Ng went the extra mile in putting this material together.

por Eslam H

20 de Ago de 2018

I got the same feedback for many of my colleagues that this course is not that important and I should start with course #4 instead, but I am glad I didn't there is a lot of insights and experiences in this course that I think it would take anyone many years to conclude by himself.

por Antonio C

14 de Abr de 2020

That's a great course to learn some practicalities of deep learning/transfer learning and multitask learning, and when to use different strategies for structuring a project. In my opinion, the course could do with a hands-on programming exercise to help consolidate the learnings.

por Milan S

1 de Jun de 2018

Sometimes its become bored who has not any experienced into working on real life ML project because without facing problem you can not understand problem in better way so i recommend course instructure to make this course with little more practical way so that it easy to digest.

por Bakr K

28 de Jun de 2020

The lack of progamming assignments hurts what could have been one of the best courses of the specialization, especially in solidifying the advices and ideas seen here. Nevertheless this course still provides valuable informations, and it's one i'll come back to later for sure.

por Hans E

18 de Fev de 2018

A bit slow going and repetitive (and some simple video editing to remove double sections would improve things). Nevertheless I'm amazed how much I learned or consolidated is just a few evenings of watching these videos. Thanks again! Looking forward to course 4 in this series.