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Comentários e feedback de alunos de Structuring Machine Learning Projects da instituição

40,792 classificações
4,511 avaliações

Sobre o curso

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....

Melhores avaliações


Jul 02, 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!).


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.

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151 — 175 de 4,472 Avaliações para o Structuring Machine Learning Projects

por Cristina N

Dec 19, 2017

Absolutely LOVED this course: with the two "case study" you can really get a sense of what does it mean to set up a real ML/DL project and how to address the problems you may (and you're very likely to) face by building up or leading a ML/DL project.

If you're thinking about learning Deep Learning, this course is absolutely NECESSARY!

por Tesfagabir M

Aug 18, 2017

This is my third course in the deep learning specialization. I have learned a lot related to different strategies with machine learning projects. The concepts are easily explained with practical examples. The assignments are also very helpful for applying in real machine learning projects. Thank you professor Ng. You are the best!!!!

por Ayomi A

Mar 01, 2018

Excellent course and very interesting !!

Allows you to analyze real ML problems and supports you with the basic and essential skills needed to develop ML algorithm and evaluate its performance and how to approach the issues that one can encounter during the iterative process, what are the options, which is the best to go with, etc.

por Ayush P

Dec 03, 2017

Really good course to develop an approach to NN problems. I thank you Sir Andrew Ng for all the courses that you have made available on Coursera. It has been an really awesome experience learning about neural networks from you. I will finish the remaining courses and recommend it to people who want to pursue a career in ML and AI.

por 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.

por 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.

por 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.

por Ahmet

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.

por 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.

por 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 team and Coursera.

por 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


por 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.

por 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.

por 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.

por 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.

por 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.

por Gopinath

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.

por 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.

por 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.

por 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.

por 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!!!

por 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.

por 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.

por 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!

por 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.