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

4.8
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Оценки: 45,678
Рецензии: 5,209

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

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.

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.

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201–225 из 5,152 отзывов о курсе Structuring Machine Learning Projects

автор: ANIKET A G

17 июля 2020 г.

The course really streamlines and puts forth a structured approach to go for delivering a machine learning solution to a problem. It helps to complete my project in 2-3 months instead of a year that sometimes some of my colleagues take. They need to look at this course. Also the interview with Ruslan was very informative.

автор: Azamat K

17 авг. 2019 г.

Really liked this course, especially the case studies, where the task is clear and possible scenarios are explained. Have to response in the most promising way using the knowledge obtained during the previous 2 courses. Really appreciate this experience. Only wish is to have more case studies in the other courses as well.

автор: Bradley W

14 дек. 2017 г.

Great course. The pragmatic insights were invaluable. I think addressing problems such as missing input data and data preparation would help. I also think a programming assignment that explores these ideas would help. You could take the sign language number exercise from week 2 and explore some of the ideas this week.

автор: Gopinath

16 янв. 2020 г.

I can confidently say that this course has content which is only unique to this course. To my knowledge no other course has topics like Avoidable bias, Bayes optimal error, Error analysis and emphasis on train, dev & test set data distribution mismatch. This course is definitely a must for any Deep learning practitioner.

автор: Deepak V

6 янв. 2018 г.

Looking at the title of this course I predicted that it will be regarding to teach me how to organise the source code files of ML project and more specifically how to build a ML project and components of deep learning project but it was all about DEBUGGING ml project so for me this was in off beat course from its title.

автор: Tony H

30 авг. 2017 г.

Extremely useful, practical techniques for deep learning projects. I feel much more able to construct my own neural networks, diagnose and solve issues with them after following this course. Professor Ng is a gifted teacher. His style is careful, methodical and never less than very well prepared and deeply enlightening.

автор: Ayan G

20 апр. 2020 г.

Its really nice to get the valuable insight of managing an AI project, this course not only thought us about deep learning, but also how to manage them efficient and take smart decision. I like the concept of Transfer learning as it can same a lot of efforts and time to build an system for complex. Thank you very much.

автор: Kwan T

1 окт. 2017 г.

I am very lucky to be able to learn from Andrew the DOs and DON'Ts of how to develop a successful practical deep neural network for real applications. It would take a machine learning developer many years of working experience to acquire any one of the topics that Andrew articulated in this course. Thank you so much!!!

автор: Konstantinos K

31 дек. 2020 г.

The course is great. It tackles a lot of problems regarding strategic decision making and at the same time important concepts such as human-level error, avoidable bias, transfer learning, end-to-end deep learning and others are being taught. The questions/exercises really test the core concepts that are being taught!

автор: Mark Z

11 июня 2019 г.

I've decided to take this course after seeing its feedback from other people and the comment which got me was the following: "This course is could be summarized as a machine learning master giving useful advice". I think it perfectly describes the course's content. This course is definitely worth investing time into.

автор: Dunitt M

10 февр. 2019 г.

Excelente curso, muy recomendado para quienes tienen una idea de Deep Learning pero con frecuencia se encuentran en situación que no saben cómo afrontar o cuál camino intentar primero. El conjunto de habilidades impartidas aquí no te harán un mejor programador, pero te ahorraran muchas horas de esfuerzo innecesario.

автор: Gaurav K

7 сент. 2017 г.

Amazing tips shared for structuring machine learning projects, which were ignored in most of the other ML books. Building a model is one thing, but tuning it to make it work better in the real world is more important which this course focuses.

Thanks Prof. Andrew Ng for the consistent support of spreading knowledge!

автор: Muhammad A

13 авг. 2020 г.

Although, this course of specialization was simple with no assignment still the case studies were quite informative. I would suggest to include a case study related to google machine learning for navigation and voice recognition. We youth can easily relate to this case study. Overall this course was a full package.

автор: Yuezhe L

19 нояб. 2018 г.

This is a very helpful class. I have been working on machine learning projects for years. This course provides methods to systematically trouble shoot problems in a machine learning project. Despite all the samples are using neural networks, the methodology can be applied to improve other machine learning projects.

автор: Danial A

3 янв. 2021 г.

Andrew NG has a peculiar style when it comes to teaching data science. I have never seen someone explaining the terms this effectively. The material in this course is a direct revelation of his years of experience and entails the unique feature of being the lessons learned from the experience. Really great course.

автор: Bernard O

24 окт. 2018 г.

Excellent course on managing through the thick of bias/variance tradeoffs. Been doing a lot just based on things I have picked up through experience, but this course puts a the quantitative rigor and discipline behind the art. The sections on transfer and end to end deep learning were eye opening sections for me.

автор: Gema P

25 февр. 2018 г.

This course is strategically very important so congrats on making it

I would add a programming assignment including transfer learning or multi-task learning implementation due to the multiple cases of use that are today in the industry.

Thanks again for making this Wonderfull material available to the community ^^

автор: BAZIL F

29 дек. 2019 г.

Very useful course for understanding nuances of AI and different useful techniques in strategizing the approaches. Extremely useful in architecting, designing and delivery of the complex solutions involving AI (even as a sub-component). Prof. Andrew Ng is always a pleasure and honor to learn from. Thank You Sir!

автор: Harvey Q

4 сент. 2017 г.

Really inspiring course, and UNIQUE. No other class, I think, provide these suggestions on the big question "what's next?" in ML projects. The videos are a bit weirdly sequenced. But they provide very systematic ways of project starting, data splitting, model evaluating, problem finding and tuning. Great course!

автор: Pedro B M

28 февр. 2019 г.

This a course on key practices one should have when developing a ML project. Once again Andrew Ng is very pedagogical, teaching sometimes complex concepts in a easy to understand and practical way. I particularly liked the case studies, where the learned concepts had to be put into practice for decision taking.

автор: Niyas M

29 окт. 2017 г.

What a great session! Full of practical advice and strategies to help you iterate fast. Prof. Andrew draws on his years of hands-on experience at top companies to put together the best practices for structuring your machine learning projects. This has been the most valuable course in this series for me so far!

автор: Nikhil K

9 июля 2020 г.

super helpful! something that's really valuable in-terms of optimally organizing the thought process i should use to approach an issue i want to solve with Deep Learning.

also, the Quizzes in this course (in-particular) were very important for me because it helped ingrain the tenets of this course in my mind.

автор: Zebin C

18 мая 2019 г.

In the course, I learned how to divide train set, dev set, and test set, and how to solve the problem of different distributions of train set and test set. Impressive is the transfer learning. Transfer learning is a very effective way to help me provide a completely different approach to solving new problems.

автор: Swapnil T

31 мар. 2020 г.

What can be better than this, a highly qualified and passionate individual explaining what he has observed and learnt from the mistakes of other professionals , those who themselves are one of the smartest brains so that we don't make mistakes or waste our time realizing that we were hitting something wrong.

автор: Jiri L

4 янв. 2021 г.

This is a really good course and material applicable to deep learning and, to an extent, also to machine learning. The course gives you a very good diagnostic and problem solving methodology for various issues with algorithm performance. So far I'd consider this to be the best course in the specialisation,