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.

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!!!

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.

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!

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!

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.

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..

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.

Special Thanks to You, Prof, Andrew, but I prefer you show us how you think when you build a machine learning model from scratch. Please make some videos of this type. Something like: Building a classification model from scratch or Building a face recognition model from scratch. I want to see your experience in practice not just the theoretical knowledge. I want to see how you handle data, choose the right model, train the model, make decisions, etc but with a real project. I would be happy if you provided some content like this, and thank you in advance.

I took this course because I was leading a project, and so far so good, the learnings from this course have set me on the right path to troubleshooting the various problems we encountered, and as well conducting effective error analysis on the project. So far, based on Prof Ng's lessons, we have been able to improve our system by a certain percentage and come to a definitive conclusion on why some of our processes are not working as they should. This course is a must-watch for not just technical project leaders, but for all machine learning practitioners.

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!

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

0 math, yet I learned so much practical engineering advice, probably more than in all of the theory classes. I am very grateful. I love these kind of classes where we learn a lot from Andrew Ng's practical invaluable experience, and strangely, they end up more difficult than the pure math classes. To me, this kind of classes are higher quality training data for for the NN inside my skull than theory classes of something encapsulated in a library ;). It would take me decades of practice to reach these conclusions. Thank you for the wisdom!

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!

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.

Out of the deep learning specialisation courses so far for me, this has been one of the most valuable. Although iterating through solutions for deep learning problems is becoming faster, it's clear that huge amounts of time can be lost spending time chasing ghosts, as there are many areas in which empirical observation is the key to success. Some of the answers to questions posed in the quiz might seem counter-intuitive, but I believe the course sets up a solid framework with which to quantify solutions and approaches.

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!

This is definitely a very important course for those who wish or already work with Deep Learning / Machine Learning. Andrew guide us into debugging and analyzing possible problems and decisions that we, as developers in Deep Learning might come to. The whole course is a very deep analysis on each step during a Deep Learning application, such as Error Analysis, End-to-End Deep Learning, Transfer Learning, Multi-Task Learning and others, it's a complete meal of information that we can absorb. Amazing teachings!

Despite not having coding exercises, I've found this course very helpful to gain more insights about the general workflow of an applied deep learning process. I think that is really valuable, as you can learn coding by searching the internet, but many times I've found myself knowing how to code but not knowing what to code. As I was hearing the lessons, I found myself thinking about projects I had been working; it would have been very helpful if I had taken this course before dealing with those challenges.

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! 👍

Very good course! This course covers a lot of the nuance around actually running ML projects. It provides useful frameworks for thinking about how to proceed when you encounter different scenarios during the lifecyle of an ML project. Importantly, the course covers things like: how to think about human level performance on a task, doing detailed error analysis on your results so you can proceed in helpful directions, when to think about transfer, multi-task and end-to-end learning, and many more topics.

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.

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!

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!

Very important course. Most of the stuff in this course is what is important for practitioners and is missing in other courses, I think most of the organizations and teams miss out on the strategy and devote far more time in wandering in wrong directions. It would be helpful if at certain points when referring to some concepts, a brief recap be given. There are too many concepts in other courses that at times you have strain yourself to recall. It is good mental exercise but it may help some folks.