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

4.8
звезд
Оценки: 47,503
Рецензии: 5,446

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

MG
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.

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.

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251–275 из 5,411 отзывов о курсе Structuring Machine Learning Projects

автор: Jorge A R H

15 июля 2020 г.

Really good course. As a machine learning practicioner I discover new ways to attack a machine learning problem. It taught me where should I focus to achive my goals faster. I think that in the exams they could give a little more explanation of why some answer is wrong. Overall an excellent course.

автор: João F

25 мая 2019 г.

Very good course. Professor Ng explains very well why some strategies are better than others and how a deep learning practitioner or team can save a huge amount of working hours by following the instructions taught in this course. There are also useful, in-depth discussions in the forum. Thank you!

автор: Lien C

4 апр. 2019 г.

Great practical insights of how to start a ML project, how to improve/optimize the system, how to identify and troubleshoot common problems in deep learning. The course provides comprehensive high level guidelines for anyone who uses machine learning, even without having any programming experience!

автор: Dariusz J

19 июля 2019 г.

The course has practical content. When took in the Deep Learning Specialization I noticed that some parts of the material were already known from previous courses. Indeed, in previuos courses the repeated aspectes are presented from a different angle, but probably there is an area for limiting it.

автор: Jialin Y

21 апр. 2018 г.

It's like understanding deep learning: a team leader's perspective. Andrew may be the first instructor to give this kind of course. Based on his experience in building practical and large scale machine learning system in Google and Baidu, the course content is highly inspiring and worth listening.

автор: Ged R

3 окт. 2017 г.

As an Ops person by nature, i like to see methodology and structure along with systematic approaches to results - be they solutions or problem solving. This course adds to that area, by providing best practices and ideas, it forms the basis from which these challenges can be addressed. Very good.

автор: Akshay M P

25 сент. 2020 г.

THE must have course for every machine learning enthusiast!! The course is very enjoyable with invaluable insights and expertise from a well-rounded deep learning practitioner. It greatly helps to clear the machine learning workflow and best practices to quickly develop, iterate and ship a model.

автор: Mihai L

28 янв. 2018 г.

This course had no programming assignments. Yet I found it amazing. It truly gives you insight into how to engineer your projects to account for real world conditions.

Liked the flight simulator analogy to this course. Accelerated learning is really the great benefit of following Andrew's advice.

автор: Gabriel L

25 авг. 2017 г.

I've done a Master degree in IA and the things covered in this course have never been addressed by any of my professors. Now I've been working in a Machine Learning team for the past two years now, and I believed these lessons would have been of great value, and would have saved me a lot of time!

автор: Ruben G

7 авг. 2020 г.

This a great course on Deep Learning, the contents are so full of interesting information, actually, this course could also be called "Everything you wanted to know about Deep Learning (but were afraid to ask)".

As always, Andrew delivers a great course, whose content is ready to put in practice.

автор: Julio E H E

16 июня 2019 г.

This course is very helpful to learn best-practices and problem-solving strategies that can help improve our deep learning algorithms. While I think the ultimate way of learning is through practice, here you can at least get a list of things to try in the future as you work on these algorithms.

автор: ANSHUMAN S

26 мая 2019 г.

Although this was a bit hard for me to understand but still through the quizzes i got an insight so as to where will these advice be applicable and where i can use what i studied.

I am thankful to the teachers and a especial thanks to Coursera for giving me the opportunity to avail this course.

автор: Virgilio E

17 дек. 2017 г.

I think this part of the specialization is a great value key, and makes the difference with other courses, self learning books, etc. The contents of this individual course helps a lot into understand and improve knowledge studied in previous and next courses. I definitely recommend this course.

автор: katherine t

14 дек. 2020 г.

A very unique course. It's like having a casual conversation at Andrew Ng's office, where random bits and pieces of industry knowledge comes up. Though the field is rapidly evolving and some of the "best practices" keep changing, the underlying philosophies still make this a worthwhile course.

автор: Severus

6 мая 2020 г.

This is the one that talks a lot about how to struture DL projects. And many methods have been taught in this course including focusing on the error control, transfer learning, multi-task learning. After learning these methods, tuning a DL project or starting a DL project will be a lot easier.

автор: Andrei N

21 сент. 2019 г.

The content, examples, assignments, and quizzes are thoroughly developed. All the courses of the specialization share the same notation and lead a student from basic concepts to complex ones helping to develop an intuition on each step. The best course on topic of Deep Learning one could find.

автор: Neil O

8 дек. 2017 г.

This is a unique course that provides invaluable perspective on how to direct a deep learning project. Its value is derived from understanding the performance metrics ( the data about the data) and acting in a data driven way. Anyone in charge of a deep learning project should take this class.

автор: Diwakar P

10 июля 2020 г.

This is really a greate course taking ont into the deep thoughts of how to structure deep learning projects. I teaches use how to analysis the various errors like human/bayes level error, training error, traing-dev error, dev error & test error, I learnt to anaylise errors and take decisions.

автор: Quentin M

1 авг. 2021 г.

Don't skip this course! This in many ways is one of the best and most important courses in this specialisation. There's lots of great advice here which is relevant to any application of deep learning. As usual Prof. Ng brings his gentle and thoughtful manner to bear on some important topics.

автор: Md R H M

16 июля 2020 г.

The lectures are arranged in a concise manner with only the necessary details. This is a shorter course than the other courses, but I learned a lot about different strategies of ML. The case study is also of top-notch which helped me to introduce to the application and various aspects of DL.

автор: Mahmoud S E

23 окт. 2019 г.

It has the best practice tips and top secret advises for Machine learning.

It really simple and clear. I love it too much.

Especially, the exams, A lot of effort is done on it. And the instructors notice which best way to absorb this deep concepts in this course by flight simulation techniques

автор: Virendra K Y

5 апр. 2020 г.

Thank you so much team and NG sir. What a simple explanation of everything. Love you guys and god bless you and your team sir. Honestly, no word to say how simply NG sir explains all the concepts. Hard work team. Love from India. and do yoga to boost your immune and stay safe from Covid19.

автор: Charles B

21 июля 2018 г.

Covers some interesting points, particularly around introducing external data to your training set that doesn't match the distribution of the dev/test sets. Andrew Ng offers practical advice for running projects using Deep Learning techniques and how they differ from traditional approaches.

автор: Tanay G

4 февр. 2020 г.

I was sceptical at first, it seemed that the course would just teach a lot of theory which won't be relevant. I am happy to say that I was wrong, the course gave me a better understanding of how to take various decisions for a particular machine learning problem. I liked this course a lot.

автор: Akash B

14 мая 2019 г.

It teaches the decision making process whenever you're working on a real- world probelm. You should grasp all the ideas into your brain very well. I think this is very important as per in the field of deeplearning.

This course is very rare, and it provides best case scenarios to test with.