Nice explanation on clustering methods.

Till Expectation Maximization, the learning is tremendous. However, once past that, everything would feel incomplete since most assignments are spoon fed after that. Rating it four stars because of initial lectures.

This course is generally good, but I do feel less smoothly guided compared to the other courses in this specialization. For most modules of this course (other than the LDA part), the lecture videos are clear as before but the programming assignments are more demanding. You will probably need helps from google, at least for the usage of graphlab's functions. But as long as you are not completely new to programming and python, you should be able to work it out fine.

However, for the module introducing the LDA model and Gibbs sampling, I find it difficult to follow. The lecturer tried to convey the concepts and intuitions without presenting the step-by-step algorithm, probably because they are too involved. But personally, I would prefer still have them to think over even if I can not understand them now.

It is also a pity that the one other course and the capstone project originally planned of this specialization are not launched in the end. I do believe the lectures will provide high-quality course content and introduce them with passion.

Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing

The assignments are excellent and help understand the algorithms and concepts taught in the course. There are some garbling in the subtitles/transcripts (including the quirky one that every time the lecturer says EM - the "EM" doesn't appear, and the following word is capitalized). As usual Graphlab Create / Sframes can't handle apply(). however mostly apply() appears in the part of the assignment that inputs files and turns them into data matrices and the explanations how to run the assignment with Scikit-Learn include pre-computed input files

I liked the slides.

This course was my first encounter with Machine Learning! The course gave me a good understanding of the different ML algorithms used in clustering and retrieval of data!

Advanced course. The material taught in this course is more advanced compared to Regression and Classification courses. You have to invest more time in respect to the previous courses. For some topics (LDA and hierarchical clustering) I had to look for other sources in order to understand the concepts properly. However, this course is a good introduction to clustering and retrieval.

Greatly enjoyed it. As with the other courses in this specialization the discussion of the subjects is impeccable, especially if you've taken some preparatory mathematics courses. The reliance on Graphlab Create is a drag though.

I did not understand LDA from the course materials.

The things I liked:

-The professor seems very knowledgeable about all the subjects and she also can convey them in a very understandable way (kudos to her since talking to a camera is not easy)

-The course was well organized and the deadlines were adjusted when a technical difficulty was found by several students

-All the assignments are easy to follow and very detailed

-The testing code provided for the programming assignments is a huge help to make sure we are solving it the right way

What can be improved:

-Some of the concepts during weeks 4 and 5 seemed a bit rushed. Although the professor explained that some details were outside of the scope of this course, I felt that I needed a more thorough explanation in order to understand better

-Some links to the documentation of libraries used in the programming assignments were lacking information on how to really use them, I wish we had some other link to worked examples too

In general I can say this was another good course for this series. Making a course like this is not easy at all and I can see that they are putting a lot of effort to produce them. All of their hard work is really appreciated on my end.

As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.

Overall is great. The LDA and Dendrograms lack quality/specificity and depth of the previous topics. So sad the Specialization collapsed at 4 courses instead of 6.

Doesn't go quite as deep into the details as some of the other Machine Learning courses from the University of Washington do. Overall though, the course covers a LOT of ground. and provides exposure to many different topics.

I would have liked to have seen an Optional section on the derivation of some of the math that we were given functions for on the Expectation Maximization section. The models in the hierarchical clustering section take longer to fit than is necessary for a course like this (more than 40 times as long as the instructions say it should take), maybe a larger tolerance for convergence should be specified?

At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.

I liked it a lot

The course was very detailed. The case study technique was rather very helpful as compared to theoretical technique. I would consider the programming assignments from medium to hard difficulty. The course could have been much better if graphlab as well as scikit coding would have been taught side by side.

Too little "case-study" approach

Need more details in the coarse. I think many of the topics need more working on, and are not sufficiently described.

Overall a good and useful course, however:

A) They could do a much better job regarding LDA, standard Gibbs sampling, and Bayesian model and inference. Many slides on these 3 topics only contained some text and the instructor tried to "verbally" visualize the related important concepts. Hence not a good use of a video session.

B) Week 1 and the 1st half of Week 6 were redundant.

C) It would be much better to have a 7-week course with more topics and may be with some optional videos on Bayesian model, HMM.

This specific course traded off depth and detail for breadth of topics. Too many ideas were quickly described and not really built up to my liking.

Feels like this course in the specialization was a bit rushed, compared to the first 3 courses. It had 2 modules (first & last) that were more like placeholders and the middle 4 modules went from concept to the maths behind the algorithm very quickly. It needs a bit of work on expanding the course and presenting a bit more slowly. Having said all that, the concepts and algorithms taught are very interesting and a good first step into the unsupervised learning section.

Great course but I think the workload could be spread across the weeks more. Also, I had a lot of trouble with the sklearn toolkit (probably due to installation issues.).

Interesting content but explanations are less clear with respect to the other courses of the ML Specialization

The course focus on a great part of researches i have never read about them or had any idea about all of it. It doesn't focus on how we implement the core functions of machine learning but it was all of benefits and very very good to me i have learned a lot of things thank you all it's very tough and challenging course for me thank you all.