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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição deeplearning.ai

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48,352 classificações

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

TG

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.

AM

22 de nov de 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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4551 — 4575 de 5,522 Avaliações para o Structuring Machine Learning Projects

por Hanling S

8 de dez de 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.

por Cheng J

20 de set de 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.

por ashwin m

1 de jul de 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

por Jithin V

3 de jan de 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.

por Silvério M P

6 de set de 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

por Vignesh S

28 de mai de 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.

por Rahul P

24 de ago de 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

por chandrashekar r

18 de set de 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.

por Gustavo S

4 de jan de 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.

por Michael F

19 de out de 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.

por Grant G

3 de dez de 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.

por Jeffrey D

31 de mar de 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.

por gjycoursera

27 de jun de 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.

por Othman B

2 de jan de 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.

por Chris M

14 de dez de 2021

The i​nformation is very useful, but the text, quizzes, and video all felt a little less polished than the previous two courses. Not a big deal, but it was notable and occasionally distracted from learning.

por Antti R

3 de nov de 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

por Samuel C

14 de out de 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.

por sam h

21 de out de 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.

por Wiebe V

30 de ago de 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.

por Sandeep P

24 de jun de 2018

The course appraises the reader of the various tricks that are needed to design nice machine learning projects. One minor suggestion would be to have some programming assignments for this course as well!

por Ed E

6 de nov de 2017

Great material but a few glitches in the videos that need editing out. I'm sure the people responsible will put it right soon though; the course hasn't been out long. Part of an excellent specialisation.

por Jon-pierre H

29 de nov de 2017

Overall the course was good, but lacked some minor information needed for the quizzes. The quizzes also included some questions that felt like they were meant to trick us rather than test our knowledge.

por Yuting S Y

2 de out de 2017

I didn't find this course as useful as the previous two. I'd still recommend the course because some interesting concepts are covered. The materials seem more intuitive. The quizzes are good practices!

por Noam S

16 de out de 2018

The course is very teaching in my uneducated opinion and will help m later in life, hopefully.

I wish the test question had been more coherent.

I enjoyed learning it, and the simulator is a great idea!

por Maciej B

25 de ago de 2017

Course is great although only in one case we have pdf's with additional lecture notes. They are more useful than ppt slides therefore it would be desirable to have them in other parts of the course