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36,275 calificaciones
3,873 revisiones

Acerca del 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....

Principales revisiones


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.

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26 - 50 de 3,833 revisiones para Structuring Machine Learning Projects

por Francis S

Aug 26, 2019

Previously, I have taken online classes before in Machine Learning by going the cheap route (Udemy, blogs, youtube) and you get what you pay for. Andrew Ng explains it the most thorough, easiest, and simplest way possible. Presentation material is very understandable. Great class for new machine learning learners. Highly recommend it. The only downside is that the programming exercises are little too easy in my opinion. I feel like the best way to get your hands dirty is to do actual projects (do your own projects). These lectures are good for intuition and background of different types of Neural Network architectures. Other than that, Great material. Thanks Andrew!

por Chou C C

Dec 10, 2017

In this course, I learned a lot about how to make right decisions when facing different problems in machine learning tasks. It helps me to review the decisions I made in the past, and also shows me a more systematic way to think about what to do next. I strongly recommend everyone interested in ML to take this course.

The only thing I'm not so satisfied with is that some questions in the quiz are quite confusing. Maybe they just have wording issue, but these questions and their corresponding answers do confuse a lot of people. I think maybe TA could take some time to address these problems in the discussion forum and help us learn even better.

por Ernest S

Oct 29, 2017

Another excellent course made by Andrew Ng. It is another perfect example of how to prepare good learning materials.

This course does not in fact expect you to write the code. Teacher is aiming not to offer you his abilities to make working system. He is offering you his deep insight and experience in making systems better and better to the point in which they meet expectactions. He discusses how to address issues you may encounter in systematic manner and where put your resources to use them in most efficient way.

If you are building machine learning models I am sure that this course pays off and can spare you many mistakes you could make.

por José A

Nov 06, 2017

This is a passive course. Don't let the 2-week course set you off. The videos in here are really insightful. They give you some of the experience that Andrew has seen throughout the years.

They will provide you with the right way on how to split the data sets, how to handle when the train, dev & test sets come from different distributions; advantages of orthogonalization; The avoidable bias, the satisfying and optimizing metrics.

By investing in this course, this will save you tons and tons of hours of work by understanding some key concepts that you will need for an effective Machine Learning problem.

por Ali A A

Sep 25, 2018

An amazing course indeed. A bit "dull" to some due to the lack of programming assignments, but extremely beneficial and insightful to anyone seriously considering to tackle an ML project. You have to appreciate the fact that while what this course covers may sometimes seem like "common sense", it is still reassuring and comforting to know that these concepts and principles are what the likes of Prof. Andrew Ng go by when they embark on an ML project.

To all who are working on making this platform what it is, I'm very confident that it is not an easy thing at all, so thank you so much.

por Daniel C

Feb 01, 2018

This course provides valuable practical advice on overcoming common obstacles in machine learning and deep learning projects. Some people might dismiss these advice as "common sense", and they would be wrong! Common sense isn't so common most of the time. In other words, there are many advice and suggestions this course offers that I hadn't thought of, but "obvious" once I learned them. Well, I need to hear them, and I'm glad I took this course. BTW , the assignments are essential. You can apply not only what's discussed in the lectures, but also learn new "common sense" methodology.

por Teyim M P

Feb 15, 2018

The course content is very theoretical but packed with very very applicable information for improving machine learning systems. The use of simulation exercises at the end of each week really goes a long way to compensate for the theoretical nature of the course content by giving learners the ability to think in terms of a real world project and seek ways to make it better. Technically speaking, I found this course more important than most practical courses that are filled with coding exercises without any additional information around making the code perform better. Great content!!!

por Ricardo S

Dec 17, 2017

This is a short high value course. It is especially good for someone who is trying to get into machine learning at a professional level, to avoid the usual pits of project structuring and time management. Highly recommended. It might seem less motivating, because it is perhaps less technical than other courses in the deep learning series, and does not have programming assignments, but in my view it might actually be at least as important as the more technical courses (if not more) in terms of allowing students to deliver machine learning projects in a professional context.

por Srikrishna R

Aug 13, 2018

This course provides insights that you normally wouldn't get reading a book alone. While it does cover the core theories behind structuring of projects, what sets it apart is the truly practical tips and tricks that you could put to use in your project right away. The guidance is actionable and draws from practical experience of stalwarts rather than draw from theory alone. The test & exercise was quite innovative too as it puts you through a real world simulation to help you understand decision pathways you would take based on situational role play. Overall 5 stars!

por David T

Dec 30, 2017

Having talked to someone who is actively working on Neural Network models, some of the insights I learned from the course looked to be helpful to them as well when we talked. I really appreciate the hands-on quizzes as well, as they gave me a chance to critically think through what I had just learned, and apply it to a real-world example. They especially helped when I got things wrong, because then I was able to rethink some assumptions I had made, and solidified my understanding of the material. I hope the next two courses are just as good as the last three!

por Donald R

Sep 23, 2017

This course ia about the practical application of Deep Learning techniques. Andrew Ng's other courses are very theoretical and prepare you with a very strong mathematical foundation for Machine Learning. This course provides practical advice and recommendations for teams building real-world applications of Deep Learning -- advice garnered over many years of work by Professor Ng and others, and, as far as I know, not collected into a single source anywhere else.

I have taken several of Professor Ng's courses. They are all excellent. This may be the best so far.

por Vishal R K

Feb 24, 2019

So far, this has been the most useful course out of this specialization! Sure, the others might offer more technical expertise, but this trains you things that cannot be taught in a class or a lecture. The application oriented case studies are extremely intriguing and challenging to a person whose knowledge might be completely theoretical. This course trains you to think in real life situations of applying a deep learning model, where to cut costs and effort, where to add more, how to optimize your model to surpass even the human level, and go further etc..

por kunal s

Aug 15, 2017

It is one of the awesome courses everyone should join as by investing time for this course you may save your time in future when you are working on real world problems as Andrew has taught his experience where people makes mistakes and how to not repeat it and save your months of time,also he have taught in details about the datasets creation and there use.And also how u can use pre-trained model for other type of dataset. Join and it will make you more curious to dig dipper and also at same time making you better than some of real experts in the industry.

por Benjamin G

Aug 19, 2018

This short course really fills in some gaps in terms of "tricks of the trade"; I think of useful information of this sort as the "force multiplier" whereby some small pieces of advice and insight from a practitioner goes a long way. I checked in a couple of machine leaning books and couldn't find equivalent advice. I particularly liked the point that was made about machine learning and certain ideas becoming obsolete (having previously done a PhD in machine learning) as I had that impression myself and was discussing it with a colleague this very week!

por Emily Y

Oct 07, 2018

I like how it discusses everything on a strategic level. Very helpful when leading AI teams in the office. I wish there were a couple more case studies on different AI topics like natural language or signal processing or dialog systems. These are hot topics in the industry and academia and would be helpful to both professionals and students working on these problems to gain some insights to these problems as well. Thank you Andrea and Team! This is wonderful and would high recommend to L&D department to add this to our data science options

por Jairo J P H

Feb 01, 2020

El curso es muy bueno, particularmente estoy muy agradecido con COURSERA, por darme la oportunidad de hacer los cinco cursos de la Especialización en Deep Learning con ayuda economica y permitirme tener acceso a este tipo de capacitacion y certificacion. Muchas Gracias…!

The course is very good, particularly I am very grateful to COURSERA, for giving me the opportunity to do the five courses of the Deep Learning Specialization with financial aid and allowing me to have access to this type of training and certification. Thank you very much!

por Anders S

Dec 24, 2017

Best applied course I have taken so far. Very practical, great to do before starting a project. I do have a suggestion for the specialisation in general. I have been working with deep learning, specifically image recognition and I have had a hard time figuring out what images I need to feed into my algorithms. Material about what type of data is needed to train algorithms correctly and overall requirements of this data would be great. I know this is done in some level, but not in a level of detail necessary for a project.

por Jaime A

Sep 08, 2017

Probably the best course to learn how to approach a Machine Learning project and deal with all the multiple challenges and issues which arise in real applications. Lots of years and experience of ML work distilled in a set of practical recommendations which can save one and entire teams months of work and computing expenses. The quizzes, based on simulated real cases, help mastering the recommendations. An ideal course for the more novice practitioners to catch up with the most expert ones in just a couple of weeks!

por Atul A

Aug 24, 2017

Great course! This is the first course I've seen that gives a "big picture" overview on *how to approach* new machine learning / deep learning projects. It dives into how to structure the project, how to separate training / validation / test datasets, how to perform error analysis when your errors are high, how to trade-off bias/variance, and when and how to apply end-to-end deep learning. In short, this course is about finding the right trails, rather than going deep in the forest. Highly recommended! 👍

por Cédric v B

Aug 14, 2019

This course contains some very essential information regarding the appliance of machine learning in a project. I think that it really discerns itself in this regard when compared to other courses. The lectures are very clear and I particularly enjoyed both exercises: the questions were very well chosen. Also, I quite like the 'Heroes' videos (also in the previous courses) as they also provide some very good information on the field of AI / ML in general as well as some practical tips on how to enter it.

por Phaneendra R

Jul 03, 2019

One of the best courses I have ever gone through, the lessons were short and to the point thus allowing me to absorb the concepts even though they were bit outside my experience. Andrew generalized the topics so effectively that I could relate similar experience to understand the concepts. I love Andrew's simplistic, repetitive, regressive approach so if things aren't clear in the first go, you can trust him to reviw them at the right opportunity. I would love to learn more on this topic from Andrew!

por Artem D

May 29, 2019

This is a very interesting course with very useful recommendations which could be also applied to ML projects. I highly recommend this material.

The only downside is that the course is structured as 1-1.5 hours of lectures and then practice quizzes (which are actually very interesting). And as for me, it becomes boring just listening without hands-on then, say, 15 minutes, despite the material itself is very interesting.

I hope that the next courses will have more practice.

All-in-all, a very good course!

por Martin K

Jan 15, 2019

This course completely wrapping up the topics from course 1 and course 2 of the deep learning specialization while presenting up-to-date (and fun(!)) "real" word evidence cases. From all the courses in the specialization, I found this one particularly compelling in terms of easy-to-grasp and the best overview of ML projects. The assignments were outstanding, making you really the feel like you truly understand ML challenges, use cases and solutions to problems.

Totally recommend this course!

por Benny P

Feb 24, 2018

This is a very good course on machine learning subjects that are rarely discussed elsewhere, namely managing machine learning project. And surprisingly, despite the easy feel of the subjects and their explanation in the video, the decision making that you have to take (and is tested in the quiz) in simulated project is hard. As project leader, given many choices of things to do, it's hard to decide what's the best thing to do, and this course shows, teaches, and trains you how to do that.

por Guy M

Sep 05, 2018

This course felt a bit out of sequence in that it left behind the more "hands on" notebook coding for a higher level "How to manage an AI team/project". This made sense when I realised it used to be the last of a three-course specialization. Aside from how it fits into the flow of the specialization (which then moves on to get technical again with CNNs and RNNs), it's jam packed full of incredibly sound advice that even experienced team leads would probably benefit from reviewing.