Mar 31, 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.
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.
By Jingxiao Z•
May 21, 2019
This is a practical course, extremely helpful for those who have met so many troubles in realworld projects. It is quite helpful for startups, where we can implement those ideas immediately. On the other hand, the transfer learning and end-to-end learning paradigms might be very useful but challeging in big companies and sectors.
By Nouroz R A•
Sep 28, 2017
This is one amazing course because it exposes you to a 'real' ML/DL problem. As a newbie I learned a lot and hope that in future I will once again do it as a ML research/development Engineering Manager. This is something very practical and now while doing big projects I will consider the learning of this course. Thanks Andrew Ng.
By Hermes R S A•
Mar 07, 2018
Consider this a course on best practices. I found fundamental advises on how to best carry a ML project from scratch, regarding the first model you should choose, how to perform on different scenarios, how to choose systematically your train/dev/test set and so on. The project simulator is a must, I wish they put more of those.
Feb 24, 2019
The teaching in this course is so invaluable for interpreting the results. Now, I believe I can understand my models' accuracy based on professors teaching. The professor teaching contains unique knowledge and experience, where you can't reach via the internet, library or asking your university professors. Thank you, Prof. Ng.
By Shehryar M K K•
Oct 23, 2017
I think this course was very valuable in teaching insights about how to think about and formulate ML/DL problems. The case study quizzes were really good and made you think. I hope coursera expands on these case study quizzes for future version of this course as well as introduce them into other courses of this specialization.
By Alessio G•
Aug 16, 2017
This course is a summary of Andrew's experience. I've yet listened this nuts and bolts from Andrew speech(you can find it on youtube) but there are some precious advice that are so much valuable. I'll recommend this course to everyone who want to start a carer in DL. Big thanks to Andrew, the Deeplearning.ai team and Coursera.
By Ankit S D•
Sep 09, 2019
This course really help me to understand exactly how to make decision to distribute the data sets, what to do with the new data set, how to examine the error, how to use previous model as a transfer model for other classification, what is multi-tasking and many more
Thank you for your support and sharing of your knowledge
By Arvind N•
Aug 12, 2017
This course was most useful as Andrew explains practical engineering challenges and valuable tips to overcome them!
As a technology architect, I am more interested in predictable, guaranteed results and can guide my my ML engineering team to make the right choices in given real-world uncertainties and engineering challenges.
By Rahuldeb D•
Jul 29, 2018
This course provides us an overview of the errors we have to encounter while solving a machine learning problem and shows us a clear direction of overcoming those. Though the contents are not mathematical but these information will help us to deal with machine learning projects in efficient way. I really liked this course.
By Wei-Chuang C•
Aug 19, 2017
The course is very practical and also leads you to learn the real challenge you will encounter while working on machine learning project. While it's easy to follow as the previous courses, you need to think more strategically. I would recommend bringing an idea or a project you are planning and apply what you learned here.
By Azamat K•
Aug 17, 2019
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.
By Bradley W•
Dec 15, 2017
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.
Jan 16, 2020
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.
By Deepak V•
Jan 06, 2018
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.
By Tony H•
Aug 31, 2017
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.
By Ayan N G•
Apr 20, 2020
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.
By Kwan T•
Oct 01, 2017
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!!!
By Mark Z•
Jun 11, 2019
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.
By Dunitt M•
Feb 10, 2019
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.
By Gaurav K•
Sep 07, 2017
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!
By Yuezhe L•
Nov 20, 2018
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.
By Bernard O•
Oct 25, 2018
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.
By Gema P•
Feb 25, 2018
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 ^^
By BAZIL F•
Dec 29, 2019
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!
By Harvey Q•
Sep 04, 2017
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!