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.
By Jagdeep S•
Oct 29, 2017
This course imparts the real world experience that Andrew gained by working in the Industry on the bleeding edge of AI and Machine Learning. This class saves at least 2 years of painful learning on your own by trial and error. I think 2 weeks on this course will put you ahead by 2 years in your path of building neural networks for solving real world problems.
By Osdel H H•
Sep 02, 2018
This course was new for me. I only had some prior knowledge about transfer learnign because I use it on my Bachelor´s Degree Thesis on image segmentation using Imagenet pre-trained weights, but all other concepts and all those guidelines of how to structure a project and how to solve the problems for make a faster and successfull iteration was really helpful
By Mohankumar S•
Sep 02, 2017
Machine Learning Flight Simulator was an intriguing adventure, you get the feel of being inside the shoes of real life AI project leads! Words can't describe Andrew and team's efforts, brilliant guys! Keep up the good work :). Really excited to see what challenges you've got in store for us in the upcoming Convolutional and Recurrent Neural Networks courses.
By Tanuj D•
Mar 28, 2020
This was by far one of the most challenging courses in the deep learning specialization as it covered a lot of practical ml implementation. I personally think that the ideas and the strategies discussed in the course will be highly useful while implementing real-life models. The assignments are very well designed and created a real-life scenario environment
Aug 17, 2018
Andrew Ng is amazing. The way he focuses on these very often overlooked details of ML projects alone would qualify him as a professional of a different category. On top of that he has an incredible ability to explain complex things in an easy way. If he was a baseball player he would be hitting 60 HR per season while pitching 40 games with a 0.87 ERA :-)
By Rashmi N•
May 19, 2019
Thanks a real bunch, Coursera for providing financial aid and bringing up this course, truly loved each and every section, coupled with quiz section at the end, is so much helpful and of course, very thoroughly made! Thanks to all the hardworking instructors and teaching assistance, and of course, coursera team for making this course so effectively! :)
By Sikang B•
Apr 01, 2018
Generally felt this course is super useful as it helped answering several questions of "why we do things this way" rather than follow the paradigm of "it just magically works". Though there are still many magic moments while learning on ML in general, I felt this course really helped broad my view and understand the overall problem space much better.
By Luo D•
Sep 15, 2017
Having finished the first three courses in the Deeplearning.ai's specialization, I find this course is the most valuable one. It is not telling you the basic algorithms like the first two courses, but telling you how to ANALYZE you project as a whole in each step, and where to go next. The first two tell you how to build, this one tells how to THINK.
By Jay C•
Mar 20, 2018
Excellent guide work by Andrew NG,
I really like the way he delivers the intuitions or insights from deep networks. The most important think when working with these kind of project is to look below find what you missed in considering higher level extraction. I'm really inspired by his work and keep the advice to improve performance for all projects.
By Abdelrahman R•
Feb 12, 2020
Maybe its different and should help us not just thinking of Algorithms and models ,we should think out of box and think of the error from different approaches as human relative to the machine, think of the data we have, think of different distribution of the data, trying to knowing with different approaches how we should care about of these error.
By Yiyou L•
Nov 13, 2017
This is a very good course. Worth taking. I am currently a data scientist and in my daily work I face a lot of data mismatch problems and I have no idea what to do after error analysis. This provides a very good guideline of how to structure our deep learning projects and what should be the thinking logics behind. Thank you Andrew I really love it.
By Nitin G•
Nov 15, 2019
Have taken a formal 1 year course from a prominent Institute but these kind of concepts were never covered there. The beauty of this course and all courses by Andrew Ng is that they are so simple and easy to understand that one can't help but only understand the concepts. Best methodology and delivery of teaching I have found online. Thanks a lot.
May 11, 2020
Excellent course and well presented material. I would like to recommend all the ML engineers to review this course before starting actual development. This course explains different intuitions and techniques with reasons what to choose, where to apply and when to apply.
Great course. Enjoyed a lot. Thanks Andrew for your precious time and efforts.
By Urso W•
Sep 08, 2017
Having followed this course I have learned how to address common problems that I have found in the evaluation of performance of my neural net based on fed datasets. I am now able to reason much better (thoughtful) on the problems that I encounter having learned some error analysis techniques which have been addressed in this course. Thumbs up!
By Ondrej T•
Dec 25, 2018
I really liked the programming assignments in the two previous courses (although, it was usually not enough challenging for me). In this course, I found "case study" assignments very useful and exciting. So far, I am very satisfied with the DeepLearning Specialization; I will definitely continue to the 4th and 5th course. Many thanks for it!
By Li-Han C•
Dec 12, 2019
I thought it's a trivial course and I didn't expect that much. HOWEVER, I must say this is one of the most important courses EVER in ML. SO MUCH I should larn before doing my dissertation. I really don't need to DIY so many things. Thank you, teacher Andrew for sharing the treasure experience. I really learn many concepts from your lecture!
By Oly S•
Jul 07, 2019
Wow. This course is densely packed with really great *practical* and well-justified advice, based on Prof. Ng's extensive experience. There's lots of wisdom here for taking the step from understanding 'in principle' how machine learning can be applied, to having practical understanding of the techniques to get it to really work in practice.
By Alejandro S M•
Feb 17, 2018
Very interesting course to avoid common pitfalls and have already some developed intuition without having worked in any ML project before.
The case studies in the quiz are extremely helpful as some concepts can be a bit confusing and they help clarify the doubts you might have in the subtleties between the different situations you may find.
By Carlos V•
Dec 26, 2017
"Structuring Machine Learning Projects" provide so many good practices in how to correctly implement Deep Learning Models, troubleshoot them and make them better, the tips and recommendations are excellent, highly recommended to anyone interested in deep learning this is a fantastic Course, thanks to everyone that make this Course possible.
By Reza M•
May 11, 2020
When you deiced to join AI teams, you need to tackle out-of-the-blue and state-of-the-art problems. Managing this kind of situations aren't easy and need different tips and tricks based on the problem statements. This course come up with brilliant ideas to make up your mind in these challenges. Great job! Coursera and deeplearning.ai
By Shivdas P•
Dec 25, 2019
This course gives a very intuitive understanding for analysing performance of neural networks and strategies to go about improving them. Also liked the introduction for Transfer Learning. The quiz which was kind of a pilot simulator for machine learning project, is excellent in understanding the decision making process for such use-cases.
By Rahul K•
Mar 01, 2018
Really well structured material! Don't be fooled by the lack of assignments, though; this course is pretty theoretically challenging. Pay extra attention to all the data distribution lectures - they are bound to come in handy in practical use. I learnt tons of really useful information from this course. As usual, hats off to Prof. Andrew!
By Raimond L•
Aug 23, 2017
This course provides a lot of interesting topics, which are general things to understand before taking on any deep learning project. I highly recommend listening to this course. It widened my view on projects I work on.
Quizzes on the other hand are bit of a mess on this course (however they are giving enough challenge to apply the theory)
By Sriram V•
Oct 09, 2019
Another set of insightful patterns from Andrew' (as well as his team') experience was stitched well together. Definitely, most of the discussions were thought-provoking for someone who is late entrant in this space. Some more reading (optional) could have added to enable us to understand more common problems in Machine Learning projects.
By Utkarsh P•
Mar 11, 2019
This course is extremely valuable for any Machine Learning student. It covers a lot of important concepts that need to be used even for simple ML tasks (not deep learning). This course provides a framework to iterate on your problems and I believe that will make the most difference in how fast you are able to achieve desired performance.