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.
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.
創建者 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!
創建者 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.
創建者 Hristo V•
Aug 31, 2016
The last weeks, we went through the material a little bit too fast.
創建者 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!!
創建者 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
創建者 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.
創建者 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
創建者 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
創建者 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!
創建者 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.
創建者 Andrey T•
Aug 11, 2016
I did not understand LDA from the course materials.
創建者 Marco A d S M•
Oct 20, 2017
As explicações poderiam ser um pouco mais detalhadas neste curto. Tive certa dificuldade em alguns conceitos apresentados, mais do que nos outros cursos.
創建者 Ayush K G•
Feb 24, 2018
At some topics more explaination (eg. Map reduce and LDA) needed although as a whole it is good course.
創建者 Christopher M•
Jul 01, 2019
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?
創建者 Andrey C•
Apr 10, 2017
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.
創建者 Koen O•
Aug 27, 2017
I liked it a lot
創建者 Jayant S•
Oct 25, 2019
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.
創建者 Baubak G•
Jul 11, 2018
Need more details in the coarse. I think many of the topics need more working on, and are not sufficiently described.
創建者 Harsh A•
Jul 18, 2018
Too little "case-study" approach
創建者 Saeed S T•
Sep 08, 2016
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.
創建者 Dmitri B•
Jun 21, 2017
Theory is cool but programming assignments requires proficient phyton knowledge. GraphLab helps but it wont be used in real life in our company :(
I found strange that often optional topics are major part of quiz, but anyway you should watch everything :)
創建者 Aayush G•
Nov 10, 2016
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.
創建者 Adrien S•
Oct 07, 2016
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.
創建者 Kayvan S•
Feb 15, 2018
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.).
創建者 Valentina S•
Aug 16, 2016
Interesting content but explanations are less clear with respect to the other courses of the ML Specialization