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

4.8
33,626 classificações
3,522 avaliações

Sobre o curso

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience. After 2 weeks, you will: - Understand how to diagnose errors in a machine learning system, and - Be able to prioritize the most promising directions for reducing error - Understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance - Know how to apply end-to-end learning, transfer learning, and multi-task learning I've seen teams waste months or years through not understanding the principles taught in this course. I hope this two week course will save you months of time. This is a standalone course, and you can take this so long as you have basic machine learning knowledge. This is the third course in the Deep Learning Specialization....

Melhores avaliações

AM

Nov 23, 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.

DC

Mar 08, 2018

Going beyond the technical details, this part of the course goes into the high level view on how to direct your efforts in a ML project. Really enjoyable and useful. Thanks for making this available!

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3401 — 3425 de {totalReviews} Avaliações para o Structuring Machine Learning Projects

por Wayne S

Sep 01, 2019

Video lectures tend to be repetitious, and can be confusing.

por Gaurav M

Jun 14, 2018

It could be little shorter module

por Francisco S R

Oct 25, 2017

The course was just a bunch of tips and suggestions. Yes, they are useful, but given the empirical nature of machine learning I would expect those tips to be accompanied by practical applications and homework.

por Dany J

Nov 16, 2017

Good content, but could definitely benefit from a more fleshed out problem to solve. The content beg for a larger concrete coding exercice project.

por Tzushuan W

Jun 01, 2019

Wordy and too abstract without hands on experience.

por Mike T

Jul 24, 2018

I wished there were exercises besides the quizzes in this course

por 臧雷

Sep 05, 2017

Most of the materials in this course is tedious and have already been taught in previous courses. But I suggest the Transfer Learning and Multi-task Learning part, as well as the end-to-end learning part.

por Peter T

Apr 17, 2018

While it was useful to see some of the best practices in ML, and the course contains practical information, the information could be delivered more concisely. Also, we get a lot of intuition, but the delivering of the material is getting less and less rigorous. The very least it would be nice to see some sources attached to each video. 3 stars may be a bit harsh, and it does not mean that I do not think it is important to listen to this course, it is more about the way of delivering the information.

por Adam S

Dec 27, 2017

Some useful insights but not much depth here.

por Apolo T A B

Nov 11, 2019

Not exactly what the title promises. In this course you will learn more about the overall approach of a ML than how to organize your data and best practices on comunicating and sharing information. (at least in week one, so far haven't started week 2).

Now I've done week 2, is much better than week 1, but still the problems presented are way more in a way of the rational behind the ML projects than Structuring the project itself, peharps a better title would be: "DEFINING GOOD MACHINE LEARNING STRATEGY APPROACHES" or something like it.

por RB

Jan 31, 2018

Good course to learn about structuring the projects and carrying out error analysis. I wish there were some assignment to work on in addition to the case study quizzes. Assignment really help us learn effectively

por Vladislav Z

Oct 08, 2017

Have mixed feeling about this one.

It is more like one week course - to few materials for 2 week.

por SAGAR B

Oct 30, 2017

The course work is really good. It has a practical emphasis. However, I did not like the quizzes (especially week 2 quiz) in the sense that the options are not very clear to understand and you end up being more confused. I hope the team works on the clarity of options for people who take it in future.

por Zijing Z

Nov 17, 2017

The topics are interesting however the content is off par.

por Evgeny S

Apr 05, 2018

I would rather expect a course more like a capstone

por Mikhail G

Oct 31, 2017

quite short, would be nice to get some practice

por Benedict B

Jul 27, 2018

ich

por Giacomo A

Jan 28, 2018

Contains some useful tips, but they are a bit too diluted - I feel like it could have lasted much less and still conveyed the same information.

por Till R

Mar 02, 2019

Some things are best learned from experience.

por everglow

Jan 27, 2019

I still feel a little confused when I have so many options to improve my NN. This course is less clearly taught than the two former to this one!

por 태윤 김

Jul 09, 2018

no funny

por John H

Sep 21, 2018

Poor video editing. Not enough graded material to feel confident that I fully understand the concepts proposed in the lectures. Definite step backwards from courses 1-2.

por Subhadeep R

Sep 25, 2018

Frankly I didn't find this to be very useful.

por David L

May 23, 2018

Zero programming assignments, but simple quizzes that will make whatever you just learned as fleeting as the morning dew on a hot summer's day. Too bad, because otherwise the material is quite interesting.

por Péter D

Oct 06, 2017

long videos saying actually very little ... disappointment