Slightly disappointed with the assignments to be honest, most of them are too easy to solve, and moreover can be just copypasted from the hints.

Great theory which lacks some demanding practice tasks.

Seems lacking some mathematical details like how to calculate Backpropagation this time

Excellent course, although it would have been good to talk more about backward propagation, after finishing this course this is the only point that is left unclear in my mind.

I finished this course with 3 weeks left in my monthly subscription, only to find out the 3rd course wouldn't be available until the day my subscription ended. I essentially paid for a specialization that didn't exist at the time of my purchase.

Great course! and according to me, the ML roadmap that best matches the one I thought to approach the ML topic based on all my experiences. So I recommend this course of Andrew to everyone.

Just as in the other courses in the specialization, this provides solid conceptual overview, but: 1) too little conceptual practice (the quizzes are short and inanely easy) 2) too confusing labs (for someone with plenty of JS, particularly web-related, but no prior python/data-science experience, I struggled to understand how the data structures were being manipulated and to intuit what the helper functions were asking me to do, and plenty of numpy/python errors that I had to interrogate for too long, distracting from the core concept. On the latter point, I laud their attempt to scaffold with hints, but this needs revision).

Enjoyed thoroughly the course. The mathematics concepts we’re well explained through exercises that help us visualize the concept behind each equation. The exercises were well thought out to help the student bridge theory to practical coding. People not familiar with mathematical coding will start to understand the pattern behind and be self-sufficient. Thank you for building this wonderful course.

This was one of the best courses I have ever experienced. There was a subtle beauty in the course's planning and Dr Andrew's teaching. The effort Andrew sir made in his teaching was quite evident, and there was a remarkable balance in the difficulty of the course - no matter whether you are a beginner or experienced with Machine learning, you will enjoy this course!

A very beginner friendly course that has great explaination on the topic. The programming assignments are really simple and I hope to see harder assignments in course 3 which would allow us to put everything we learn into practice

The course was fantastic! I really enjoyed every part, every video, every quiz, every optional lab, every assignment of the course. It was a pretty memorable ride to have come this far.

The content is excellent but some more emphasis must be given on the discussion of the codes in the Jupyter Notebooks otherwise it'll become less appealing to the once who don't have a good grasp over Python.

One of the best explanations of the backend of most advanced ML algorithms. The instructor is the best of the best. Learning new and Advanced Algorithms surely helps you to understand the newer technology and help your hunt down a good job in your future. Thanks to Coursera, Stanford, Deep Learning.AI and Andrew Ng for this very special course and for all of your hard work in making this course go smooth and easy to understand.

Amazing, the explaination of the neural netowrks part is amazing wtih very good examples.

The Bias and Variance and the error analyis parts are very good.

I loved giving an idea about more advanced implementations like transfer learning and ensemble learning.

Really good introduction to machine learning notions and algorithms such as neural network and decision tree, with many informatives details about how to improve them and examples of implementation.

excellent course, for me it's the best online course you can see anywhere

Nicely presented, however the assignement and the material was too basic.

I would have liked a more rigurous introduction to the subject, that being said I feel I did get something out of this course.

There are way too many errors on the video's subtitles - not grammatical mistakes, but words completely out of context or even incorrect formulas.

I was really hoping more detailes on xgboost and unfortunately the course level was very elementary

Dr. Andrew Ng and his Team have created a solid ML course that is both beginners friendly and highly intuitive. The theory,just like the first course in the specialization, has been explained in a very lucid and friendly manner. They have created a course that is really good at imparting all the necessary theory to learners without bagging them down with all the complicated maths related to them, which is something I have found a very good thing. Plus , they also touch upon all the open source ML libraries that is used by the industry and enthusiasts alike, which is something I really appreciated.

All in all, this is one of the best ML course out their and a must learn for every beginner ML enthusiast.

i love the way Andrew Ng teaches, i remebered a while back, i tried learning about decision tree from the internet and i could remember , i left the tutorial more confuse. i love the way he presents the learning intuition of each topic. for everyone watching & taking the course, watch out for the "intuition" of each topic. Andrew Ng does a great job of demystifying ml concepts ( than most youtubers that dive aimlessly into jupyter notebook using libraies without explaining the concepts) and also partake in the quiz and assesment, they are great help.

it was a nice experience learning, Thanks Andrew Ng, DeepLearningAi, Stanfordonline

A great course that explains and walks you through actually coding principal supervised learning algorithms. Would appreciate if the description on calculating back propagation from the original course had been included. Then again, this version of the course I actually got through :). All in all I felt the assignments where at a level that I could quite quickly work through, and find help when I got stuck. Grateful that Andrew Ng and his team made this course series subject so accesible and doable.

Nice and informative course on the basics of advanced machine learning. Assisted with well programmed lab assignment, I got to learn a lot of machine algorithms from diagnostic ml, bias and variance and their importance for calculation of losses and finally decision tree equipped with several tools like random forest, XGboost etc...The information gained on completion of this course is surely going to help me come a long way in doing my phd project effectively with the help of machine learning

This is by far the best advanced machine learning algorithm course I've ever taken. Using simple examples to convey several no so intuitive concepts. Some of the concepts that was not 100% clear prior to this course are now crystal clear. But honestly, I would say for those who have zero experience in the field of machine learning and coding might find it hard to take in, especially the part when trying to write and understand the algorithm from scratch in the lab.

This course is well designed for a beginner who wants to get the intuition of deep learning and see what is under the hood. By taking this course you will have a better understanding of principal concepts in deep learning and artificial intelligence and deep learning does not appear as a black box. The lab assignments are well designed to teach you better the whole concept. In a nutshell, well done Andrew NG, Stanford University, Deep learning AI, and Coursera!

Another excellent course. Dr. Ng's presentations are super clear and easy to follow, graphics/animations are superb.

What I think this course needs is a bit more time/explanation on backward propagation. Here it was touched upon only briefly and in an optional lab, and it was not clear how it tied back into neural networks algorithms. I had to learn this separately on my own by finding articles on Google. Thanks and looking forward to the next course!