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!).
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.
por Zhiming C•
This Part of study is a aimed to improve the skills during the Modelling and Calculation. In the realistic problems, people need time to get familiar with the process of how to build a sophisticated network. And the time to learn these experiences could be long. This course give us a lot of useful information and tricks. It saves our time and reduced the hardness for the work! It's great!
por Eleanna S•
I wish there was more such cases that I can learn from. I found this course very valuable. Thank you :)
I would be interested in participating in research. Do you think that Coursera could help with creating PhD degree/ applied research. I would like to improve the world by applying the knowledge I gained from this specialisation. Do you think Coursera could help with something like this?
por Jason T B•
This course should be mandatory for any machine learning practitioner, researcher, or student. Ng shares excellent insights and provides a clear structure for thinking about how to manage our most valuable resources in machine learning -- labeled data! The course discusses the concepts in a deep learning context but I would recommend even for those not working on deep learning problems.
I took this course soon after completing the Machine Learning course, before starting the Neural Network and Deep Learning. And found it extremely helpful, the simulator approach takenup in the course is absolutely spot-on and unique to this course (as compare to any knowledge source on internet).
Andrew NG has poured in his tacit knowledge and made it explicit in the best possible way !
por RUDRA P D•
This course gives insight to all the errors and their analysis, different approaches to deal with problems in machine learning and also working of different models such as Face recognition, Speech recognition and Automated driving models. Andrew sir explains all this concepts in a very learnable manner. I do recommend this course to those who are going to build their first ML model.
por Ber L C•
One of best courses I have taken on Coursera. There are not much available online resources to learn about how to structure and manage a Machine Learning projects. I would like to express my appreciation for all of the hard work and dedications professor Andrew Ng and his team spent on designing such a great course with understandable lectures as well as well-designed assignments.
por Armando G•
This course is the most hands-on deep learning class I have seen so far... and have taken a lot. Most courses focus on the technical details of feedforward, backpropagation, activation functions, etc. but this is the only one I have seen where guidance is provided on how to tackle real-life situations. So far, the BEST course I have takes on deep learning projects tips and tricks.
por Dennis O•
This course is light on math and programming but loaded with great advice that I have already been able to put into practice at work. Some things are lessons I have learned by being in the field for a few years and others are lessons that might have taken a while to learn on my own. This course has extremely valuable real-world advice that will impact the work I do right away.
por Artyom K•
I understood such concepts as: evaluation metric, percentage of distributions, estimating train and dev set errors,
training a basic model first,
carrying out error analysis
on images that the algorithm got wrong,
algorithm will be able to use mislabeled example,
dev and test set should have the closest possible distribution to “real”-data, and so on.
por Sherif M•
This course offers insights into organizing and structuring machine learning projects. It is different than the other courses of this specialization by not going to much into technical details. I found it still very rewarding since Andrew offers some very niche tricks that can help researchers in practical application of machine learning and deep learning algorithms.
The topics discussed in this class are very closely associated with the title `Struturing Machine Learning Projects`. These topics are more than just concepts, I think they would be very useful in real projects (Though I haven't done one :) ). There are a lot of use cases discussed in the course. Hoping in the near future, I have an opportunity to use them in practice.
por Michalis P•
This course was smaller and a bit more theoretical than the previous two courses. Although the lectures give you a good insight on error analysis, things to check in order to optimize your model and finally how you can use a pre-trained model to solve a different task - of the same input data type.
Thanks both to the instructor and the crew for this great series of lectures.
por Bill A•
Really changed my thinking about how to run an ML project. I just wish my projects were the kind that could exploit these methods to the fullest. They're more like the autonomous driving example. There are parts that DL is useful for (particularly sequence learning with RNNs) but big parts that aren't (e.g. use of probabilistic graphical models). Anyway, awesome course!
por Linghao L•
Lots of principles and skills about how to organize machine learning projects and diagnose problems. Especially for the error analysis part, you will definitely save much more time in solving these errors than you expected by following the suggestions taught by Andrew. Thanks Andrew, I really learned a lot from your awesome deep learning courses and felt closer to industry.
por Chetan P B•
This course is just magical. It covers so many concepts that would require years of experience to gain. Thanks to Professor Andrew for sharing his great knowledge with us. The bias/variance and train and dev/test distribution concepts are very well explained with examples. Also, the quiz helps to practice these concepts which require a better understanding of all of these.
por Pedro H d O P•
Great course as always! Andrew Ng is a great teacher, and he actually can inspire all of us on being better professionals (and researchers) on the field. The idea of the case studies was great! It was very fun to experience how it is to be part of deep learning projects and the decisions associated with this. Congratulations for all of you guys from coursera! Thank you!
por Adrian S•
This short course focuses primarily on non-technical aspects of deep learning projects. The value of this subject matter is the focus on aspects that can make or break the success of a machine learning project. Given the fact that as much as 80% of deep learning efforts never make it "into production" (Gartner et al) spending time on these issues is highly recommended.
por Sahaj J•
Initially, I was bored from some initial lectures. But later, I found that this is one of the most important course in the specialization because it dives to you the handful of experience in a single course which one gets after many years of practicing machine learning. At the end of this course, I am very much enlightened with the content and journey of this course.
por Amanda W•
Loved this course as well. Presented very difficult material in a simple and easy to figure manner. Excited for more! Thank you to those who dedicate their time to making this course available, and taking the time to answer questions regarding the material. It is much appreciated and I highly recommend these courses to those who wish to learn about Deep Learning.
por Mohammed M•
Really great course. It is very helpful to gain knowledge on the basic strategies to consider while approaching a Machine Learning problem. The assignment quizzes present you with a real-world ML problem (case study) and asks you questions on what you would do when presented with different situations. So that's a great way to get some insight on how things happen.
por Swakkhar S•
This is a great course, unlike many other courses where you put 1/2 lines in between the code completions and pass the assignments. This one has got a number of issues where one has to be able to think about the problem and the data/model/metrics on hand to analyze and take further steps. Once again this one is from one of the top instructors of the world. thanks!
por Nihar P•
This course has given me insights into the importance of choosing a better ML pipeline. Not only knowledge of ML is important. We must know when and where and how to apply it our your problem. This course taught me more about that. Thanks to Coursera, if I would have taken this class in school I must have missed this gemstone information.
Thank you, professor NG.
por Ventsislav Y•
Awesome course! I really like the explanations by Andrew Ng. This course gives you skills about how to make error analysis on your models, how to build a machine learning strategy, importance of single evaluation metric, satisficing and optimizing metrics, setting up the train/dev/test distributions and many other topics. Highly recommend this course to everyone!
por Himanshu B•
This course is surely gona help if planning to learn deep learning.Gaining knowledge is not the best part unless you don't know how to apply the knowledge. This course is all about how and where to apply machine learning and deep learning concepts with much more practicing in real life case studies. Thanks alot for providing such a great content and case studies.
por Mukund C•
Excellent course. I loved the "flight simulator". I found them challenging. However, some of the questions were worded confusingly, so I got the answers wrong. There is no point in trying to "trick" the test taker by confusing wording in the question as well as in the answers. But, I think this course provides a pragmatic approach to machine learning projects.