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.
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!).
by Reza M•
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 Raja S C•
The concepts taught in this course are giving very basic foundations which are essential to build deep learning career. I no longer scared to talk confidently about a model in terms of bias, variance, error etc. Though this course was scheduled for 2 weeks, because of interest that it created, I am able to complete it in a day. Thank you.
by Shivdas P•
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•
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•
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•
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•
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.
by Rishubh K•
Really unique content. People do talk about this stuff but providing access to these learnings in a structured manner i amazing. I feel I could now lead my efforts in DL project much more efficiently. I felt the case studies were amazing. I wish we had more of those available to us to practice. But, nonetheless, great work. Thanks much!
by Marcio R•
Excellent course overall! The course structure is very well made, Andrew is an amazing teacher and explains everything in a very detailed and intuitive way. The tests are a great way for practicing what was explained in the lectures. Strongly recommend this course to anyone interested in the topic and that have the required background.
by Subhasis M•
This is an excellent overview of the points that someone taking up an ML/DL project should keep in mind. Though this is not a comprehensive guide, which is understandable given the stipulated duration online courses like this are meant for, this is a definitive guide to give someone a nice head start into structuring his ML/DL project.
by Pablo G G•
Nice intuitions of what to do when you need to improve model, and believe me, you will! :D If you set up your local jupyter lab and start playing with deep learning, you will quickly see that this course is gold in order to optimize you DL algorithms!(its all about getting that loss to 0.0000001! :P) Don't understimate this teachings!
by Dmitry R•
This, in my opinion, is the most important course in the specialization! It teaches you how to plan your machine learning project, which errors and challenges can rise during implementation and how can you deal with them. Personally, I feel it helped me a lot as I currently try to plan my machine learning project as part of my thesis.
by Fasih U•
I learned a lot about different strategies to chose for getting fast and much better out come from this course. Also downloaded the book mlyearning written by Dr. Andrew. So that i will have all this in my hand when i will need this strategies to review. Thank you Andre Ng for giving this much information. You are the best I love you.
by Ankit K•
thanks for providing good insights on how to approach a machine learning application and where not to waste valuable efforts. I think Mr Ng has been very thoughtful to setup the structuring part as a dedicated course which highlights the importance of setting right goals and not to lose our direction during the development iterations.
by Kunjin C•
Compared with the previous two courses in this special, this course is more practical and useful when we are actually trying to solve real-world problems. After taking this course, one will have a clearer mind in terms of making the most out of data from different sources as well as coming up with better solutions to certain problems.
by Cristina N•
Absolutely LOVED this course: with the two "case study" you can really get a sense of what does it mean to set up a real ML/DL project and how to address the problems you may (and you're very likely to) face by building up or leading a ML/DL project.
If you're thinking about learning Deep Learning, this course is absolutely NECESSARY!
by Tesfagabir M•
This is my third course in the deep learning specialization. I have learned a lot related to different strategies with machine learning projects. The concepts are easily explained with practical examples. The assignments are also very helpful for applying in real machine learning projects. Thank you professor Ng. You are the best!!!!
by pedro o•
This is a great course for anyone new to machine learning. It focuses on the core challenges one may face while carrying out machine learning projects. Overall, it is a must take for people new to the field,professionals,hobbyists,etc .Thank you Andrew Ng for being a great instructor, I look forward to completing the specialization.
by Ayomi A•
Excellent course and very interesting !!
Allows you to analyze real ML problems and supports you with the basic and essential skills needed to develop ML algorithm and evaluate its performance and how to approach the issues that one can encounter during the iterative process, what are the options, which is the best to go with, etc.
by Ayush P•
Really good course to develop an approach to NN problems. I thank you Sir Andrew Ng for all the courses that you have made available on Coursera. It has been an really awesome experience learning about neural networks from you. I will finish the remaining courses and recommend it to people who want to pursue a career in ML and AI.
by Jingxiao Z•
This is a practical course, extremely helpful for those who have met so many troubles in realworld projects. It is quite helpful for startups, where we can implement those ideas immediately. On the other hand, the transfer learning and end-to-end learning paradigms might be very useful but challeging in big companies and sectors.
by Nouroz R A•
This is one amazing course because it exposes you to a 'real' ML/DL problem. As a newbie I learned a lot and hope that in future I will once again do it as a ML research/development Engineering Manager. This is something very practical and now while doing big projects I will consider the learning of this course. Thanks Andrew Ng.
by Mohab S A•
Exceptional, one of a kind strategic course for ML practitioners. The amount of wisdom and knowledge shared in this concise course would definitely save any budding ML engineers from the common pitfalls that many teams may still face. It also sets the foundation stone for cultivating prospective machine learning project leaders.
by Mirna M A•
the best course course so far in terms of (error analysis, how to deal with training/ dev/ test sets and what the symmetry of distribution means, how to split data set in the best way, how to be able to use an algorithm again in another deep learning project, how it's important to correct the incorrectly labeled data set, etc )
by Hermes R S A•
Consider this a course on best practices. I found fundamental advises on how to best carry a ML project from scratch, regarding the first model you should choose, how to perform on different scenarios, how to choose systematically your train/dev/test set and so on. The project simulator is a must, I wish they put more of those.