Avis et commentaires pour d'étudiants pour Machine Learning: Clustering & Retrieval par Université de Washington

Very interesting but the LDA and Gibbs sampling for LDA concepts were not easy to understand. May be we could find a simpler way to explain them. Nonetheless, with all these concepts and practical case studies learned in this specialization, we can start solving real world problems. Thanks once more!

Comme les précédents dans de cette spécialisation, ce cours est très riche et donne les clés pour utiliser des outils complexes et puissants. Toutefois, un peu plus de détails sur certains aspects, notamment théoriques, pourraient améliorer la compréhension de certains chapitres plus techniques.

I think all the advanced concepts presented in this course were a little bit rushed. Maybe I would have been better had we received more information, meaning more lecture materials. But over all, I feel really good about choosing this Specialization.

Like all the courses in this specialization so far, the material has been good. The reason for only 4 stars rather than 5 is the difficulty in getting questions answered in a timely manner. There don't seem to be any active mentors for this class.

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.

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

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.

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!!

This was a really good course, It made me familiar with many tools and techniques used in ML. With this in hand I will be able to go out there and explore and understand things much better.

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!

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.

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

I'm disappointed that courses 5 and 6 of the specialization were cancelled. The cancelled capstone was why I purchased this specialization package.

This topic was very deep and I learnt many complex algos. Would suggest to have some more examples for the algorithms presented in this modules.

Very good course. Too long interval between modules make hard for non-Python developers. Easy to forget some of the Python structures.

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

Good course overall.

Starting to get more on the side of being mostly implemented and only needing to insert a line or two.

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

More detail on theory behind LDA and HMMs would have been useful. Otherwise, another brilliant course!

Good survey of the material, but assignments are superficial and don't test thorough understanding.

Great course. Some week were tough others too easy, but general a very interesting course.

The last weeks, we went through the material a little bit too fast.

Excellent course with very detailed explanations and assignments

Great Course!

Too bad we don't have the last 2 courses....

I did not understand LDA from the course materials.