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

4.8
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46,489 classificações
5,332 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.

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.

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5176 — 5200 de 5,277 Avaliações para o Structuring Machine Learning Projects

por Kenneth C V

7 de Out de 2020

Complex Material

por Siwei Y

28 de Nov de 2017

就两周的课, 我不知道算是凑数吗

por Mohit S

15 de Jul de 2020

Not that good.

por Fotsing B K

25 de Fev de 2018

to theoritcal

por Yide Z

17 de Dez de 2017

too much bugs

por David B

19 de Ago de 2019

No Homework!

por Sean L

6 de Out de 2019

Bit tedious

por Leticia R

11 de Ago de 2018

Bit boring.

por Wouter M

13 de Jun de 2018

A bit short

por Zhen T

19 de Dez de 2019

Too simple

por Gonzalo A M

16 de Jan de 2018

Too short.

por Sunil S

26 de Mai de 2020

Knowledge

por My I

15 de Mar de 2019

too easy

por Артеменко Е В

3 de Set de 2017

Too easy

por vamshi

28 de Ago de 2020

useful

por Jalis M C

7 de Jan de 2021

good

por Debasish D

15 de Mai de 2020

Good

por Sajal J

29 de Out de 2019

okay

por KimSangsoo

17 de Set de 2018

괜찮음

por Benedict B

27 de Jul de 2018

ich

por Shawn P

8 de Jun de 2018

k

por Daniel S

19 de Mar de 2018

Definitely not worth paying for (and I literally completed this in one afternoon). Thankfully I did not pay, so it was not that bad value in fairness.

In honesty the lack of value from this course actually says a lot about Andrew Ng's original Machine Learning course, which was consistently excellent. Actually coding in Octave for that class cemented a lot of concepts as well, which this course does not.

The title of the course suggests this is pitched towards more advanced students who already know about Machine Learning but maybe not so much about best practices. This feels far too basic for that demographic. The practices are sensible though and useful, if maybe overly focussed on massive datasets as opposed to the ones that Google *doesn't* deal with on a daily basis. Things like SMOTE could have been mentioned as well, for example.

TL;DR: This feels like a missed opportunity. My advice is don't take it if you've done Andrew Ng's ML course. Google things after that and wait for a decent course that's pitched towards intermediate students.

por Gil F

17 de Nov de 2019

Notwithstanding the great video lectures this course's assignments were poorly composed:

Firstly, there are no programming assignments! I understand the material here is mostly conceptual, however subjects such as 'Transfer learning' and 'Multi - task learning' should be given as a programming assignments. In 'Transfer learning' you need to modify an existing model, which I think is a good tool for a student. Hopefully we will use it in future lessons. Lastly some of the questions in both 'quizzes' have many complaints in the forum and the same complaints reappear yearly, therefor it's a bit annoying no measures are taken to modify the questions so they will be clearer.

por Alexander D

16 de Abr de 2020

This course was pretty poor. Too many of the lectures are repetitive, and the examples given to discuss the concepts seem overly simplistic. It would be far better if AN actually discussed previous cases and what pitfalls to watch out for. For example, it's useful for practitioners to understand human component features that he mentions. He's probably seen a lot of instances in which engineers came up with great ideas that ended up differentiating a mediocre-performing algorithm from a far better one. He could also discuss go into greater case study detail of instances in which transfer learning/muti-task learning worked well or not.

por ananth s

1 de Out de 2018

Very verbose with hand-wayy examples. The 18 minute lecture was the hardest Ive tried to not fall asleep. The second quiz has extremely badly written questions with multiple choice answers. Very ambiguously worded QnA. Don't mistake this review for the whole DL specialization though. Andrew's DL specialization course is brilliantly structured and an excellent primer for folks such as myself just getting into DL. It is only this section on structuring ML projects which is a little bit of a drab.