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

47,188 classificações
5,420 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

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.

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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4476 — 4500 de 5,377 Avaliações para o Structuring Machine Learning Projects

por Juan Z

9 de Nov de 2019

This course is less pratical and theoretical. I don't mean it is not helpful to me. I think this course might be helpful as guideline when I hand on the real project in future

por elie a

4 de Nov de 2019

very good course, but I felt like it was lacking one more week of course to get deeper knowledge about how to really get data sets and how to set them up for real applications.

por Christian V

18 de Jul de 2019

you may think because the course is shorter will be much easier but the videos has a lot of information to process. I am excited to tried this techniques on real applications!

por Ambrose S O O

25 de Mai de 2019

A good course. Provided general high level thinking and reasoning for quick problem solving, data management, multi-tasking, transfer learning, and error reduction techniques.

por Sayantan A

22 de Mai de 2018

Not as exciting as the previous courses, but informative nonetheless. A section for handling imbalanced or skewed datasets would be useful, especially for multi-task learning.

por Aleksi S

22 de Fev de 2018

Not as deep into details as the two first courses in the specialisation, but nevertheless I learnt a lot of techniques that I hope will be feasible when I work on AI projects.

por Charles S

28 de Nov de 2017

Excellent lectures and notes as always. Great insights and clearly explained. I think we could have used a programming exercise on transfer learning at least in this section.

por Akanksha D

7 de Jan de 2018

More coding exercises could be included with much more mathematics background explained. Videos could be made a little shorter. There is redundancy in the some of the videos.

por Juan M

4 de Jan de 2018

Good overview of how to structure ML projects with great practical advice. I wish the course had includes a programming lab to help us try out and practice some of the ideas

por Aravindh V

29 de Ago de 2020

Good content. The tips and tricks a experienced AI practitioner has was shared. But at least one programing exercise applying all the concepts learnt, would have been great.

por Luis J P M

12 de Jan de 2020

In the first quiz, the comments about why an answer is correct are too simple. On the contrary, in the second quiz, the comments are really good and give us better feedback.

por Uddhav D

2 de Jun de 2019

I feel more Examples should be given regarding the variable and bais tuning, also Error analysis videos should be a bit in-depth. Everything else is as good as it can get :)

por Jean M A S

27 de Out de 2017

The simulations were very good to build a good intuition about setting up a machine learning project.

But I regret that we didn't have coding exercises. 4 stars for this one.

por Carlos S C V

15 de Abr de 2020

Me gustó el curso, pero creo que algunas lecciones fueron un poco más largas de lo necesario. Debo agregar que me gustaron mucho los simuladores, creo que me ayudaron mucho

por Vinod S

19 de Nov de 2017

Helps clearly in understanding practical aspects of deep learning. An additional week, highlighting the aspects of productionizing a deep learning project would have helped

por Vinay N

12 de Jul de 2020

Since I myself am working on a few projects, the concepts here are somewhat useful in error reduction. Especially when the models are used to automate medical applications

por Palathingal F

28 de Set de 2017

A unique course to understand the process of establishing a ML project. But lacks tools information and a more structured definition of the process. A bit too theoretical.

por Mahnaz A K

2 de Jul de 2019

Thanks for the practical tips and insights from real projects.

Your pool of heroes of deep learning is very skewed. If the field is so skewed, then it's a bigger problem.

por Vivek V A

13 de Fev de 2019

Good course for the ones who already started developing ML systems. This will help us in improving the ML systems and identify what can be done for which kind of problems

por Ivan L

25 de Jun de 2019

Most of the material was quite useful, but some was, perhaps, too obvious. Also, some things were discussed too thoroughly, and, in my opinion, that was a waste of time.

por Алексей А

14 de Set de 2017

Would be great to obtain more concrete information.

For example, instead of "requires much more training data" to obtain "requires ~1'000'000 samples instead of ~100'000"

por Rafal S

22 de Jul de 2019

Excellent content overall. However, reiterates some of the knowledge already presented in the two previous courses of the specialization. Lacks programming assignments.

por Amir R K P

7 de Dez de 2018

I wish there was more examples, visualization and depiction of work with referral to papers or experiment here. or perhaps a bit of project management, data management.

por Pete C

24 de Jun de 2018

Enjoyable, but felt a little less challenging and more hastily assembled. Regardless, the material is valuable and as always, a pleasure to be instructed by Andrew Ng.

por Lars R

29 de Ago de 2017

The course material is relevant and useful, however, I agree with other reviewers that these 2 weeks should rather be a 1-2 weeks addition to one of the other courses.