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Вернуться к Structuring Machine Learning Projects

Отзывы учащихся о курсе Structuring Machine Learning Projects от партнера deeplearning.ai

4.8
звезд
Оценки: 45,248
Рецензии: 5,150

О курсе

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....

Лучшие рецензии

AM
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.

TG
1 дек. 2020 г.

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.

Фильтр по:

4426–4450 из 5,094 отзывов о курсе Structuring Machine Learning Projects

автор: Nick R

7 янв. 2018 г.

Informative, but theoretical. After 3 courses I'm looking to do some hands-on work of my own.

автор: Bobby

24 окт. 2017 г.

Lecture was good but no programming assignment for this course. I took out one star for this.

автор: Ramachandran C

12 окт. 2019 г.

Loved the practical guidance provided by Andrew Ng on large-scale machine learning projects.

автор: Arthur J

20 июля 2019 г.

It would be great to have some materials to be able to go back to once done with the course.

автор: Kyle S

23 окт. 2017 г.

This is a great course. It will be an invaluable reference when tackling real-world problem.

автор: Yang Z

20 окт. 2017 г.

Some videos don't end properly and instead give you a black screen to stare at for a minute.

автор: Neel K

19 окт. 2020 г.

It was better to include some more case studies. This was a better real-time understanding.

автор: Michael L

10 июня 2020 г.

Sometimes, the explanations/advices given were too lengthly and contained some repetitions.

автор: Amielle D

24 июля 2019 г.

There were some typos throughout the course, but the core topics were still discussed well.

автор: Tariq A

12 янв. 2019 г.

A good quality course, would have loved to have some programming exercises to go with this.

автор: Ankur K

13 нояб. 2017 г.

It would have been a little better if some assignments were also provided with this course.

автор: Rohini H

9 июня 2020 г.

still with some more example & more simplest way to solve them .with simple basic examples

автор: Martin B

17 мар. 2018 г.

Again, excellent, but proof reading of the test and proof-viewing of the videos is needed.

автор: Sudeep K

7 апр. 2020 г.

It could be more detailed. More Code intense! However, the course was really informative.

автор: Abhishek P

24 сент. 2019 г.

Initially a bit hard to understand but repeating the session helped to grasp the concepts

автор: Pedro L A V

26 февр. 2018 г.

Good course, but there are too many small topics in each week and no hands-on assignment.

автор: Blake C

20 сент. 2017 г.

Not quite sure, but there are some problems in exam. Hopely fix them as soon as possible.

автор: Bharat M

24 июля 2020 г.

A good theoretical course to help remember the nuances of how to structure a ML project.

автор: Vikas C

20 июня 2019 г.

It was a nice course, it can better if some demo codes are used as an example separately

автор: Shuo W

3 янв. 2018 г.

Pro: useful practical suggestions;

Con: language used in quiz should be further polished.

автор: Ridvan S

15 окт. 2017 г.

"Chillout course", but "test-by-real-cases" is very exiting and very fine idea. Strong 4

автор: riad s

21 сент. 2017 г.

I wish there was some practice assignments related to the concepts learnt in this course

автор: Vinay K

5 февр. 2020 г.

Info about the approach in applying DL/ML concepts to various scenarios were explained.

автор: Vishal G

19 дек. 2019 г.

Interesting course, but all this could have been summarised in the first course itself!

автор: Hao W

16 июня 2019 г.

This course is not so well organized as the previous two. Everything else is very good!