Nov 22, 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 30, 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.
By Diwakar P•
Jul 10, 2020
This is really a greate course taking ont into the deep thoughts of how to structure deep learning projects. I teaches use how to analysis the various errors like human/bayes level error, training error, traing-dev error, dev error & test error, I learnt to anaylise errors and take decisions.
By Quentin M•
Aug 1, 2021
Don't skip this course! This in many ways is one of the best and most important courses in this specialisation. There's lots of great advice here which is relevant to any application of deep learning. As usual Prof. Ng brings his gentle and thoughtful manner to bear on some important topics.
By Md R H M•
Jul 16, 2020
The lectures are arranged in a concise manner with only the necessary details. This is a shorter course than the other courses, but I learned a lot about different strategies of ML. The case study is also of top-notch which helped me to introduce to the application and various aspects of DL.
By Mahmoud S E•
Oct 23, 2019
It has the best practice tips and top secret advises for Machine learning.
It really simple and clear. I love it too much.
Especially, the exams, A lot of effort is done on it. And the instructors notice which best way to absorb this deep concepts in this course by flight simulation techniques
By Virendra K Y•
Apr 5, 2020
Thank you so much team and NG sir. What a simple explanation of everything. Love you guys and god bless you and your team sir. Honestly, no word to say how simply NG sir explains all the concepts. Hard work team. Love from India. and do yoga to boost your immune and stay safe from Covid19.
By Charles B•
Jul 21, 2018
Covers some interesting points, particularly around introducing external data to your training set that doesn't match the distribution of the dev/test sets. Andrew Ng offers practical advice for running projects using Deep Learning techniques and how they differ from traditional approaches.
By Tanay G•
Feb 4, 2020
I was sceptical at first, it seemed that the course would just teach a lot of theory which won't be relevant. I am happy to say that I was wrong, the course gave me a better understanding of how to take various decisions for a particular machine learning problem. I liked this course a lot.
By Akash B•
May 14, 2019
It teaches the decision making process whenever you're working on a real- world probelm. You should grasp all the ideas into your brain very well. I think this is very important as per in the field of deeplearning.
This course is very rare, and it provides best case scenarios to test with.
By Haoxuan Q•
Jan 26, 2018
I love this course very much and I would strongly recommend this course to other DL colleague. It is truly that DL is a highly empirical process which needed to be more systematic. In this course, I have learned many methods to make DL more controllable and predictable. Nice Job! Thanks!
By K R•
Jul 9, 2020
Well, once again I'm so happy and very satisfied of the content proposed in this Course by Prof. Andrew Ng. Thanks a lot for this valuable content and for always making it easy to understand. I'm getting more and more knowledge in DL which happens to be very usefull for my Phd project.
By Pedro F d C•
Mar 23, 2019
In my experience with Machine learning, we usually spend more time checking the algorithm than checking the best distribution of our data. In this course, Professor Andrew teaches us the need and obligation to create a correct distribution of our data with examples from the real world.
By Mohd S A•
Feb 28, 2018
Extremely helpful for a beginner so as to think like a machine learning problem solver. I think there should be more quiz added to this course with scenario like given in two quiz. I have never enjoyed any course so much by taking same quiz again and again to get better understanding.
By Hiep P•
Dec 14, 2017
In the bloom of Deep Learning/Machine Learning industries, know how to build a project is more important and a priority to know what knowledges to build that project. Break the problems, take each step follow the guide and avoid common pitfalls in process, this course will satisfy you.
By Javier E•
Apr 30, 2020
this is definitely the best course i had taken. it has just 2 weeks, but it was the hardest. i will definitely come back to see the teachings here explained to check up if i'm thinking correctly so i don't make much mistakes in taking a direction in projects.
By Elena P•
Sep 1, 2017
The case study format for quizzes was highly effective in helping me uncovering gaps in my knowledge that I didn't know were there. I would have liked to see at least one more case study per week. One per week just wasn't enough.
Overall good course with a few minor video glitches.
By Carlos A B R•
Jul 22, 2019
I found this course really interesting because it gives many details on what path to follow to achieve better results not only depending on the amount of data we have but also taking into account some small details that can make a difference when starting machine learning projects.
By Dharam G•
Jul 2, 2018
A very well systematic approach explained, to structure ML projects.Can be grasped and implemented by anyone, let it be a beginner or some expert.Really liked the idea of case study in quiz. (Wait ! How about extending this idea into some coding exercise ? Would be some real fun !)
By Andrew M•
Oct 10, 2017
There is no coding in this course, but you learn a lot of how to design a Deep Learning Study. I learned a lot about the distribution of Training/Dev/Test sets and how to diagnose problems when a neural network is not performing as well as anticipated or if it is performing well.
By Tyler K•
Aug 27, 2017
Outstanding course. Many of the points made in this course mirror the hard earned knowledge I gained back when I worked on Dynamic Rank search engine focused neural networks.
This may end up being my favourite of the 5 courses but let's see if the last two have more math first. :)
By Alexios B•
Aug 20, 2017
This part of the specialization is short but it includes a lot of valuable information. Many of the tips are quite basic engineering best practices which most engineers should find natural, but some are very specific to deep learning and these are particularly useful to newcomers.
By Brad M•
Aug 22, 2019
This is truly some information you'll never get in a standard class setting; this is more similar to compiling years of ML experience into short packets of advice that will guide your decisions for years to come. Extremely helpful, and recommended for all deep learning engineers.
By WALEED E•
Jan 17, 2019
This course is really what any PhD would need to conduct his research in more time saving and efficient manner. It would be great if coding was accompanied (even if only running and watching results) to touch all aspects of analysis and suggested improvements could be visualized.
By George B•
May 13, 2021
Great course. I had a couple of ML courses at University, but nobody ever explained those concepts: orthogonalization, the data mismatch problem and what to do with it, different versions of human-level performance, end-to-end learning pros cons (everyone just talks about pros).
By Kanishk S•
Jun 27, 2020
To Andrew and team (mentors and organizers), I am glad I opted for this course! You guys give such great insight on approaching and solving a Deep Learning problem, I don't think I would have ever found a better introductory course on Neural Nets. Thank you so much, everyone!!!!
By Aditya V B•
May 18, 2020
One of the most important course in this series . This course actually helps you visualize the problems and standstills you might face when you are working on a model in real life. It also talks about practical solutions to improve your model that are valuable in the tech world.