Aug 25, 2016
excellent material! It would be nice, however, to mention some reading material, books or articles, for those interested in the details and the theories behind the concepts presented in the course.
Jan 17, 2017
Excellent course, well thought out lectures and problem sets. The programming assignments offer an appropriate amount of guidance that allows the students to work through the material on their own.
von Bob v d H•
Oct 02, 2016
Some of the interesting topics discussed in this course could be treated substantially more extensive and detailed in order to get a better grip and understanding on them (e.g. Gibbs sampling). After this course, it is a bit dazzling how much different algorithms and methods are available for clustering and retrieval tasks and this course easily could have been subdivided into two or three separate courses on the same topic with a more detailed treatment. Still, about many interesting subjects a tip of the iceberg has been brought to you ... it tastes so good that you would like to have much more!
von Marcin W•
Aug 09, 2016
Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.
von Michael B•
Sep 04, 2016
Good survey of the material, but assignments are superficial and don't test thorough understanding.
von George P•
Nov 21, 2017
Overall one of the best courses I have had in my life. It was very well structured. The material was a little bit more advanced than the rest of the courses of this specialization and therefore more in-depth explanation need to be given especially in the LDA module. In a nutshell it was a positive experience both watching the videos as well as doing the quizzes and the programming assignments
von MARIANA L J•
Aug 12, 2016
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.
von Iurii S•
Nov 26, 2017
Good course overall.
Starting to get more on the side of being mostly implemented and only needing to insert a line or two.
Jul 26, 2016
Great course. Some week were tough others too easy, but general a very interesting course.
von charan S•
Jul 30, 2017
Nice intuitive course with lots of understanding.
von Andrey T•
Aug 11, 2016
I did not understand LDA from the course materials.
von Maria V•
Aug 02, 2016
The specialization has a good quality on average. I started doing this course immediately after it went open. I had a feeling that the quality of the course went down (questions were often unclear and it took time to figure out what is expected as an answer). However, many problems were solved quite fast and teaching stuff is really helpful.
I still would like to see more about MapReduce in-depth in this course. I did not have a feeling that it was covered sufficiently (only theory, no hands-on material). In general, hands-on material was great and useful.
von Sundar J D•
Sep 26, 2016
Great course and awesome teaching by Prof. Emily Fox. Prof. Fox did a great job of teaching some of the really tough components (GMM, LDA, etc) in simple and lucid style (like always) and that made it easy to understand and comprehend those topics.
The one thing that I felt had gone down compared to the previous 3 courses was that for some of the topics, the material felt too short and felt like it was cut down to fit within the 6 weeks course duration. I would have at least liked some extra reading material or references especially for GMMs, LDA, Gibbs Sampling, etc.
von Adwait B•
Jan 26, 2018
Great Course! Tough topics well taught
von Kostyantyn B•
Nov 07, 2017
A high quality, intermediate difficulty level course. The instructors are obviously very knowledgeable in this field and strive to pass their knowledge and skills onto the students. One of the major advantages in my opinion, is the fact that the authors decided to include a number of advanced topics, which you normally don't find in an introductory level course on the Unsupervised Learning. The exercises seem to revolve mainly around the Natural Language Processing, which is fine by me, for two reasons. First, it is a very challenging part of the Machine Learning. Second, NLP is in high demand in the industry. So, I see no downsides here. Plus, there is only so much one can squeeze in a 6-week course...
I would however like to mention that I wasn't entirely happy with the way the Latent Dirichlet Allocation and the Gibbs Sampling were explained. This was the first time I heard about these techniques and I found them fascinating. I understand that these are challenging topics that require a more advanced math for a serious discussion. But I still think it would be worth including perhaps an optional video and/or exercise to go deeper into this subject. I am sure some students would appreciate it; I know I would...
In summary, it is a great course to take. It will help you better understand the theoretical foundations and boost your practical skills in the Unsupervised Learning.
von Ahmad A•
Mar 31, 2017
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!
von Yaron K•
Sep 30, 2016
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
von Farrukh N A•
Mar 17, 2017
Great course on machine learning, however, left us in middle of learning, Recommender System + Deep Learning Capstone is missing
von Liang-Yao W•
Aug 24, 2017
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.
von Hristo V•
Aug 31, 2016
The last weeks, we went through the material a little bit too fast.
von Rajkumar K•
May 27, 2017
Clustering & Retrieval was a lot tougher compared to courses on regression & classification because the match concepts behind this course were too complex. Nevertheless Emily tried to make this course as intuitive as possible
von Gilles D•
Aug 12, 2016
Still a very good course.
Week 4 was very tough. The general concept can be understood from a 10,000 feet altitude but the lesson and programming assignment need to be reviewed, maybe with a slower step by step example.
As some other student mentioned, it was... "brutal".
Other than that looking forward to the next course in the specialization!
von Usman I•
Dec 29, 2016
I am taking all courses in the specialization, and this is my fourth. I have been having a great time with materials by both instructors so far, until I came to week 5 of this course.
Despite repeated viewing, my understanding of LDA is non-existent. The first section is fine, but starting from "Bayesian inference via Gibbs sampling," for me at least, the method of instruction has gone off a cliff.
I strongly suggest soliciting feedback from learners that narrowly targets the material of this week 5 to determine if it's just me or if this is a wider problem. If it is the latter, perhaps it is time to redesign the lessons of this week.
von Siva J•
Feb 26, 2017
Good and deep dive into ML!
Absolutely disappointed that the course was delayed and the promise to take it through Course 5 and Capstone Project didn't come through.
Not at all happy with that!!
von Koen O•
Aug 27, 2017
I liked it a lot
von Mehul P•
Sep 11, 2017
Nice explanation on clustering methods.
von Michele P•
Sep 02, 2017
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.