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

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47,359 classificações
5,436 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

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.

JB
1 de Jul de 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

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4576 — 4600 de 5,397 Avaliações para o Structuring Machine Learning Projects

por Nikolai K

3 de Out de 2017

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

por Shashank S S

8 de Jul de 2019

Learned various ways to structure ML projects in industry.

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

por Leonid M

5 de Out de 2017

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

por aman a c

18 de Mai de 2020

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

por 김진수

25 de Fev de 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.

por Tim S

25 de Fev de 2018

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

por Bill T

24 de Fev de 2018

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

por Edward M

24 de Dez de 2019

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

por Mohammad H

17 de Dez de 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

por Riley

8 de Abr de 2019

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

por Ioannis K

14 de Ago de 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.

por John E M

31 de Mar de 2018

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

por Saurabh D

26 de Ago de 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.

por JEREMY S

7 de Jun de 2020

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

por Alhasan A

1 de Jun de 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.

por aditya g

21 de Fev de 2018

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

por Huang C H

24 de Nov de 2017

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

por Priyanka T

22 de Out de 2017

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

por Bingnan L

1 de Fev de 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.

por ahmed B

20 de Set de 2021

It was a great course, but it lacks programming assignments and the quizzes were great (I really miss the programming assignments)

por Zheng Z

25 de Abr de 2019

I think a little bit more programming homework can help me better understand the concepts, but other than that everything is good.

por Giovanni C

13 de Fev de 2019

It's a good course to gain an initial understanding on the role that different real-world considerations play in Deep Learning NN.

por Daniel C K

8 de Set de 2017

Good course, covering interesting topics. Seemed too easy without enough content to make you feel like you mastered the subjects.

por Prithvi M

18 de Abr de 2020

The course was very helpful. But I found some concepts to be repetitive. But it helped me to understand how to tackle the errors.

por Eche I

28 de Out de 2018

Very good insight on how to approach machine learning projects and the kind of things to look out for and tackle as they appear.