Nov 23, 2017
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.
Mar 19, 2019
Though it might not seem imminently useful, the course notes I've referred back to the most come from this class. This course is could be summarized as a machine learning master giving useful advice.
创建者 Manish H•
Oct 30, 2018
Excellent course - a unique short course where you'd get tons of insights from one of the top AI/ML experts Andrew Ng about how to curate data and structure your ML projects. Lot of practical and actionable tips.
Most useful course of the entire specialization to help you understand soul of the AI/ML development, you'll appreciate it even more if you have some experience of real-life AI/ML projects.
创建者 dhirendra k•
Oct 12, 2018
Thank you for providing this course. This course is something different, it takes you away from the technicality of the algorithms and makes you focus on a different but very important aspect of ML problems, i.e. error analysis. The professor is once again great in compiling all his practical real life experiences in teaching a subject which is not commonly found in other online training curriculum.
创建者 Glenn B•
May 31, 2018
Great topics and discussions.
I get the dynamic aspect of writing the lecture notes in the videos, however the lecture notes should be "cleaned up" in the downloadable files (i.e., typos corrected and typed up). Additionally, the notes written in the video could be written and organized more clearly (e.g., uniform directional flow across the page/screen rather than randomly fit wherever on the page.
Aug 25, 2017
This course was helpful in basic undestanding of how to evaluate the data from deep learning models.
It took very diffrent aproaches like the precision and recall metric and even get faster evaluation with a f1 score. It was also helpful to get insight on diffrent types of errors which could show some direction how to optimize dev and test sets and why it is possible to pass beyond human performance.
创建者 Lin Z•
Mar 29, 2019
very good guidance on how to start a machine learning project, including many interesting discussions including how to choose the size of training/test/dev set, how to analyze the errors, how to deal with mismatched distributions of test/traning/dev set by adding a training_dev set and how to do end-to-end and multitask training. The contents are well exercised by two well defined case studies.
创建者 Michael M•
Oct 29, 2017
This is the best series of ML that I have taken so far on Coursera. Andrew Ng is a master at instructing others. I cannot say enough about this series, you would need to take the series to comprehend what I am trying to say. Somedays I watch and I am just amazed how Andrew takes a concept and turns it comprehensible at such a fundamental level. Great course it deserves more than 5 stars!!!!
创建者 Parab N S•
Aug 25, 2019
Excellent Course on how to structure the Machine Learning projects so that the developers do not waste time following a random trial and error approach and rather take on an approach which is proved to work well in improving the accuracy of the model in spite of the changing requirements and data. I would like to thank Professor Andrew N.G. and his team for developing such a wonderful course.
创建者 Chanel C•
Aug 19, 2018
This course was very interesting. The examples are good chosen and the exams have great questions (they are summarising everything from the lessons). Great suggestions and also personal tip. I'm studying and I'm learning a little bit of these neuronal systems and machine translation which are based on language while your examples were more visual like the car case for example. Thank you :)
创建者 Eleanna S•
Mar 18, 2018
I wish there was more such cases that I can learn from. I found this course very valuable. Thank you :)
I would be interested in participating in research. Do you think that Coursera could help with creating PhD degree/ applied research. I would like to improve the world by applying the knowledge I gained from this specialisation. Do you think Coursera could help with something like this?
创建者 Jason T B•
Aug 18, 2018
This course should be mandatory for any machine learning practitioner, researcher, or student. Ng shares excellent insights and provides a clear structure for thinking about how to manage our most valuable resources in machine learning -- labeled data! The course discusses the concepts in a deep learning context but I would recommend even for those not working on deep learning problems.
Mar 18, 2018
I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning. And found it extremely helpful, the simulator approach takenup in the course is absolutely spot-on and unique to this course (as compare to any knowledge source on internet).
Andrew NG has poured in his tacit knowledge and made it explicit in the best possible way !
创建者 Manh T D•
Apr 01, 2018
One of best courses I have taken on Coursera. There are not much available online resources to learn about how to structure and manage a Machine Learning projects. I would like to express my appreciation for all of the hard work and dedications professor Andrew Ng and his team spent on designing such a great course with understandable lectures as well as well-designed assignments.
创建者 Armando G•
Sep 30, 2018
This course is the most hands-on deep learning class I have seen so far... and have taken a lot. Most courses focus on the technical details of feedforward, backpropagation, activation functions, etc. but this is the only one I have seen where guidance is provided on how to tackle real-life situations. So far, the BEST course I have takes on deep learning projects tips and tricks.
创建者 Dennis O•
Dec 17, 2017
This course is light on math and programming but loaded with great advice that I have already been able to put into practice at work. Some things are lessons I have learned by being in the field for a few years and others are lessons that might have taken a while to learn on my own. This course has extremely valuable real-world advice that will impact the work I do right away.
创建者 Artyom K•
May 19, 2019
I understood such concepts as: evaluation metric, percentage of distributions, estimating train and dev set errors,
training a basic model first,
carrying out error analysis
on images that the algorithm got wrong,
algorithm will be able to use mislabeled example,
dev and test set should have the closest possible distribution to “real”-data, and so on.
创建者 Sherif M•
Apr 11, 2019
This course offers insights into organizing and structuring machine learning projects. It is different than the other courses of this specialization by not going to much into technical details. I found it still very rewarding since Andrew offers some very niche tricks that can help researchers in practical application of machine learning and deep learning algorithms.
Mar 05, 2018
The topics discussed in this class are very closely associated with the title `Struturing Machine Learning Projects`. These topics are more than just concepts, I think they would be very useful in real projects (Though I haven't done one :) ). There are a lot of use cases discussed in the course. Hoping in the near future, I have an opportunity to use them in practice.
创建者 Michalis P•
Oct 18, 2019
This course was smaller and a bit more theoretical than the previous two courses. Although the lectures give you a good insight on error analysis, things to check in order to optimize your model and finally how you can use a pre-trained model to solve a different task - of the same input data type.
Thanks both to the instructor and the crew for this great series of lectures.
创建者 Bill A•
May 15, 2018
Really changed my thinking about how to run an ML project. I just wish my projects were the kind that could exploit these methods to the fullest. They're more like the autonomous driving example. There are parts that DL is useful for (particularly sequence learning with RNNs) but big parts that aren't (e.g. use of probabilistic graphical models). Anyway, awesome course!
创建者 Linghao L•
Jan 03, 2018
Lots of principles and skills about how to organize machine learning projects and diagnose problems. Especially for the error analysis part, you will definitely save much more time in solving these errors than you expected by following the suggestions taught by Andrew. Thanks Andrew, I really learned a lot from your awesome deep learning courses and felt closer to industry.
创建者 Pedro H d O P•
Feb 24, 2018
Great course as always! Andrew Ng is a great teacher, and he actually can inspire all of us on being better professionals (and researchers) on the field. The idea of the case studies was great! It was very fun to experience how it is to be part of deep learning projects and the decisions associated with this. Congratulations for all of you guys from coursera! Thank you!
创建者 Amanda W•
Sep 13, 2018
Loved this course as well. Presented very difficult material in a simple and easy to figure manner. Excited for more! Thank you to those who dedicate their time to making this course available, and taking the time to answer questions regarding the material. It is much appreciated and I highly recommend these courses to those who wish to learn about Deep Learning.
创建者 Ventsislav Y•
Dec 22, 2018
Awesome course! I really like the explanations by Andrew Ng. This course gives you skills about how to make error analysis on your models, how to build a machine learning strategy, importance of single evaluation metric, satisficing and optimizing metrics, setting up the train/dev/test distributions and many other topics. Highly recommend this course to everyone!
创建者 Himanshu B•
Jul 06, 2018
This course is surely gona help if planning to learn deep learning.Gaining knowledge is not the best part unless you don't know how to apply the knowledge. This course is all about how and where to apply machine learning and deep learning concepts with much more practicing in real life case studies. Thanks alot for providing such a great content and case studies.
创建者 Mukund C•
Oct 15, 2019
Excellent course. I loved the "flight simulator". I found them challenging. However, some of the questions were worded confusingly, so I got the answers wrong. There is no point in trying to "trick" the test taker by confusing wording in the question as well as in the answers. But, I think this course provides a pragmatic approach to machine learning projects.