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学生对 提供的 Structuring Machine Learning Projects 的评价和反馈

44,693 个评分
5,059 条评论


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....


Jul 1, 2020

While the information from this course was awesome I would've liked some hand on projects to get the information running. Nonetheless, the two simulation task were the best (more would've been neat!).

Mar 30, 2020

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.


76 - Structuring Machine Learning Projects 的 100 个评论(共 5,009 个)

创建者 Chan-Se-Yeun

May 1, 2018

This course introduces some general principles for developing a deep learning project. It points out the difference of setting of train/dev/test sets between deep learning and traditional machine learning. That's a practical advice. And it's notable to include human performance and regard it as Bayesian bound, almost the best we would expect an algorithm to achieve. That saves you from spending unnecessary time to make a subtle improvement. Learnt a lot!

创建者 Stanley C D

Sep 8, 2018

In this course I learned about ways to approach some of the real world challenges that I have already faced on some of my own projects. For example, what actions should you consider when you find a significant number of labeling errors in the dev/test sets that affects your ROC. I also was motivated by the last module on end to end training and the interview with Ruslan Salakhutdinov to pursue an end to end training idea that I have been thinking about.

创建者 刘尧

Nov 1, 2018

Great Course! Many students will choose to skip this course since they think there are less knowledges than other course in the 5-course specialization. But I have to tell you: this is the best course in the specialization, because you can learn a lot knowledges especially skills and experiences in practice from this course that you can't learn from other books, courses or universities. BTW, I'm not telling that the other 4 courses are not important.

创建者 Daniel S

Dec 17, 2017

Andrew Ng is brilliant! I have never seen such a great tutor in my life. He bring extremely useful concepts and explains them so easily in a way the concepts stay in your mind.

Like the backprop algorithms he talks, he has learned so much from his old course and he has made great improvements to focus on New people. He sure has a good deep network up his brain that has gone through lot of iterations (without overfitting) with beautiful set of features.

创建者 Kryštof C

Nov 7, 2018

It is very good probe to practice. I would very appreciate to take this course before I have started in machine learning. It would help me to realize some mistakes I have maid before. On the other hand, for people, who have some experience with machine learning, some chapters are being over-explained, as the topics are quite clear to those people. Overall: I would recommend this course to everyone, who wants to start with his/her own NN training.

创建者 Teguh H

Nov 29, 2017

No coding at all. But this is one of the best course on AI, because it does not talk about coding or anything, but most importantly, the one thing that is not taught by many others. Experience of Andrew Ng trials and errors in approaching ML projects. How to create structure, how to observe what results to see. In short this course is like 'how to save time in doing AI projects and make optimal use of it, avoid trial error which can cost months.'

创建者 Luis C G

Oct 19, 2017

Despite of its relative simplicity (from a technical point of view), it is probably one of the most practical courses I have taken in Coursera. Even though it only mentions deep learning, the overall methodology can be applied to any machine learning work. It is important to get familiar with the heart of the models, but it is probably even more important how to work on an end-to-end machine learning project. In summary: Highly recommendable!


Nov 18, 2020

Hello learners, and respected teachers ! As I go through this course, I was not clear about how to use test set. train set and dev set, was not able to rectify about how to break data set for so closely prediction. As we have teacher like Andrew ng, we won't miss anything about structuring machine learning project.

This course is really helpful for me and will recommend to all learns who want have command on ML & Deep Learning.

创建者 Danilo Đ

Jan 4, 2018

Unlike most of the Deep Learning knowledge which can be found in literature and other MOOCs, this course provides you with insights that can only be acquired trough (often painful) trial and error. Here you learn how to approach Deep Learning projects, how to avoid most common mistakes, and how to quickly identify errors in your model.

Do yourselves a favor, and finish this course before taking on your very first DL project.

创建者 Johnathan T

Sep 3, 2017

This course is my favorite so far. It has really given me a way to systematically and strategically set up a machine learning experiment and iterate in a way that make sense. For me the toughest part of ML projects has always been figuring out where and how to start. Now that I have some solid guidelines to follow, I don't feel as anxious about jumping into a new problem and it turning into a wild goose chase. Thanks a lot!

创建者 Tamim-Ul-Haq M

Jul 17, 2020

Such an amazing course. This course should be done by every Deep Learning researcher or enthusiast. If the previous course taught how to debug Neural Networks then this course teaches how to perform advanced debugging on the dataset. The contents of this course are invaluable and not taught in most institutions and they are not known to the wider community that actually follow the guidelines and techniques presented here.

创建者 Shankar G

Jul 2, 2018

Wow! This course was more of real time application scenarios and the kind of tweeks to build, transform learning plus multi-task learning was excellent. The end-to-end learning with a split approach of solving was really something new I found in this learning. Not to forget the application level quizzes were so tricky it was challenging to understand and interpret the possible solutions but, was great learning experience.

创建者 Kayne A D

Feb 4, 2020

More of a general comment for the specialization but I love the Andrew and the teaching team have set up the content delivery. Simple, clear and well-paced delivery with consistent use of well-considered examples. On top of that, the summaries are great representations of key concepts. I am greatly appreciating the entire specialization and seeing the bigger picture in terms of why it is structured as such. Thank you!

创建者 Andrei K

Apr 21, 2020

I find this particular course in the whole specialization especially useful so far. Andrew teaches great strategy that helps think and act on deep learning projects in a more systematical way, and does so with crystal clean examples. Quizzes in this course, similar to flight simulator, are great at ensuring you can apply the principles you have just learnt and see where your understanding is a little bit vague.

创建者 Robert K

Nov 18, 2017

Fantastic lectures combined with case-studies for real world applications. In this course you don't program, but don't underestimate the ability to abstract out and systematically assess your thinking. This could speed up your project development and save you tone of time. Any potential employers would also be happy that you know some practical aspects of implementing a deep neural network for a particular use.

创建者 Shringar K

Jul 28, 2019

Honestly speaking, this is the best course in the whole deep learning specialization. This course is the one which tells us what to do as a Deep learning engineer in real world scenario.

People can do the coding and everything, but without proper directions the product might fail.

Andrew Sir has given his expertise in a very neat and compact way, good enough for starting our own research or whatever we want to.

创建者 George Z

Aug 4, 2019

Amazing 3rd course, I learned so much related to error analysis, bias, variance, data mismatch, data synthesis as well as transfer learning, multi-task learning, end to end deep learning and more. I really loved both case studies in the end of each week. The interviews especially with Andrej Karpathy was my favorite :) Excellent best practices and strategies that you don't learn from any other course or book.

创建者 Johan B

Sep 22, 2017

This course in the specialization is less about how to build a model but gives you a structured way of how to approach a deeplearning project. It shows how much some manual (and maybe boring) counting can speed up your project and that starting with a simple model and iterating on that often outperformes very detailed thinking about your project at forehand.

The practical tips Andrew shares are very valuable!

创建者 Sebastian E G

Aug 18, 2017

Liked this way more than I thought I would. Machine learning project management is vital in a professional setting (I would assume), and I often leave it as an afterthought. It's not just building the fanciest model, it's about how to iterate from an okay model to your best model in an efficient manner. This course teaches you what to look for with your results and pinpoints what areas to focus on to improve.

创建者 Johannes B

Feb 27, 2018

Very nice introduction to the aspects of a machine learning project that is not covered other places, but is very important. Most of it is very intuitive and comes as no surprise, but it is still very usefull to collect it into a single course. It is a good resource to have in case you are in doubt about how to structure your project, where to focus your energies and how to make progress in a systematic way.

创建者 Kyle H

Jan 4, 2018

Great course by Andrew Ng, coming from his Machine Learning Course and seeking to work on Kaggle Competitions, this course provides all the knowledge necessary to approach any machine learning problem (with or without a team), and efficiently work towards a better algorithm. It's almost as if he gives you the tools necessary to optimize yourself which in turn allows you to efficiently optimize any algorithm.

创建者 James J

Sep 30, 2020

This great course provided quite a few good ideas for addressing scenarios that may arise when constructing a deep learning model. In terms of improvements, would suggest updating the name of the course to 'Structuring Deep Learning Projects' rather than 'Structuring Machine Learning Projects,' as the model-specific techniques (and examples for other techniques) all mention types of Deep Learning models.

创建者 Rohit K

Jul 6, 2019

Hello Andrew, I am a big fan of you. Learning from your every course. Very unfortunate that I can do that remotely only.

One thing that I want to mention - Can we have lecture notes on coursera, just like the way used to in CS229 that we can read before coming to next lecture. I found that that was very useful in understanding when things get harder.

Thanks hope we can improve coursera in that matter.

创建者 Christian C

Jul 2, 2020

Based on personal experience with other courses on deep learning, much of the insights focus on what I'd call the "technical details" (e.g., maths and computations, using libraries, training strategies). I recommend this course for focusing on the "non-technical" aspects.

I find the lessons in this course helpful in streamlining the planning/strategizing process in the projects that I have been part of.

创建者 Claude C

Jun 8, 2019

Good engineering tips, tricks, bolts and nuts, very useful! Andrew Ng is more dedicated to engineering and best practices that are very important in the machine learning field which is not only theory (a lot less than some believe or pretend) but very empirical, with a lot of practices, try and and error, recipes. Don't be snob, maths are awesome but good engineering and best practices are crucial too.