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Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера

Оценки: 48,213
Рецензии: 5,530

О курсе

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....

Лучшие рецензии


22 нояб. 2017 г.

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.


30 мар. 2020 г.

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.

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5001–5025 из 5,497 отзывов о курсе Structuring Machine Learning Projects

автор: Mares B

17 нояб. 2020 г.

A little short, maybe more hands on exercises?

автор: Ed G

8 нояб. 2020 г.

Concise course with some interesting concepts.

автор: Tulip T

23 июля 2019 г.

Quite helpful when you start a new ML project.

автор: Ramakrishnan v

4 нояб. 2018 г.

The session were simple, could be more complex

автор: Caique D S C

30 июля 2018 г.

very good course, could be less massive though

автор: vivin v

11 дек. 2019 г.

I want a program exercise like in 1-2 courses

автор: Dionysios S

30 нояб. 2018 г.

I would like to see more practice assessments

автор: Luis E R

31 июля 2019 г.

Very useful concepts that few people address

автор: Jun P

22 апр. 2018 г.

Kind of boring than the cnn and rnn class ..

автор: John H

29 авг. 2017 г.

Useful content, could be much more succinct.

автор: vijaykumar

15 мая 2020 г.

This course is awesome and good knowledge .

автор: Alfredo M

14 мар. 2018 г.

There were no practical coding homeworks :(

автор: Igor C

14 февр. 2018 г.

A little less dense than the other courses.

автор: Rajesh M

11 окт. 2017 г.

Can reduce some of the repetitive material

автор: JEROME D

21 сент. 2020 г.

Maybe add 1 question at the end of videos

автор: Mr. S A

12 сент. 2020 г.

a bit slow and no programming assignments

автор: Shriniwas S U

2 мая 2020 г.

Satisfied with course. Thank u Instructor

автор: Hamidreza C

7 янв. 2020 г.

Good course, nice case studies, liked it.

автор: Gaurav A

26 авг. 2019 г.

Great course, good structure, nice theory

автор: Akansha B

3 авг. 2020 г.

Was good as an intro could be hands on..

автор: David A

19 нояб. 2018 г.

Didn't get any practice coding sessions.

автор: Yunfei D

5 мар. 2018 г.

Why is there no programming assignments?

автор: John K

19 сент. 2017 г.

I would have liked more hands o examples

автор: Zhen L

9 сент. 2017 г.

Provide many suggestions about practice.

автор: Michael F

20 апр. 2018 г.

I would have liked more coding modules.