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 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.
创建者 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.
创建者 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.
创建者 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.
创建者 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.
创建者 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!!!
创建者 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.
创建者 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!
创建者 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!
创建者 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.
创建者 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..
创建者 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.
创建者 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!
创建者 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
创建者 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.
创建者 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!
创建者 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! 👍
创建者 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.
创建者 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!
创建者 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!
创建者 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!
创建者 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.
创建者 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.
创建者 Maximiliano B•
Jan 02, 2020
In this module professor Andrew NG teaches several strategies based on his vast experience to help you deal with real world machine learning projects. Most of the information is of great value and it is difficult to find organized like that in another website. I have really enjoyed the two case studies proposed and they are very interesting to help you review the concepts studied. Finally, professor Andrew NG explains the content clearly and it is a pleasure to watch his videos.
创建者 Michail T•
Sep 04, 2018
This is another awesome course teached by the best instuctor (prof.) in the net for ML and DL technologies. Knowing how to divide the dataset in the appropriate sub sets and doing the right error analyisis, is the main goal every developer or scientist in this field tries to achieve. This course teaches all this and additional concepts like transfer and multi-task learning which are essential techniques to improve productivity. I would give six stars if there were any.