24 août 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.
16 janv. 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.
par Ferenc F P•
25 janv. 2018
Very good course. Even though I had some machine learning background, this course provided new insights and new algorithms, like KDTree, Locally Sensitive Hashing, Latent Dirichlet Allocation, and mixture of Gaussians. the only drawback is that with scikitlearn, not always you get the same results as with GraphLab. Thanks for the instructors for this great specialization.
par Stephen G•
13 août 2016
Yet another really excellent course in this series - the best online course I have ever taken. I really appreciate the fairly high level on which it is taught, and the speed with which they go through the material - it is not here to entertain or waste time, but to get straight to the point - what can I do, and how do I use open source tools to do it?
par Feng G•
8 août 2018
Emily is an extremely awesome instructor. For those who have some background in statistics, biostats , econometrics and math and want to study machine learning by themselves, these modules can be an outline that introduce basic topics in machine learning.
I'm looking forward to see more advanced courses in these topics from Carlos and Emily.
par Miguel P•
13 juil. 2016
I loved the previous 3 courses and what I saw in this course so far seems pretty interesting. I'm really sad that Coursera decided to block access to assignments for not paying users. I really wanted to continue with the specialization but I already purchased another specialization, so I'm going to have to put this course on hold for a now.
par Alessio D M•
1 août 2016
Very nice course, and a great grasp on clustering techniques. If I could just suggest something to improve, it would be the section on LDA and Gibbs: it's very high level and it would be really nice to have some more technical insights on those techniques (perhaps with optional sections, as for other topics).
par Muhammad W K•
22 oct. 2019
A great course to get the grass-root level understanding of Clustering and Retrieval tasks and going beyond to Unsupervised learning and the core concepts related to it. And starting from the basics all the way to some of the advanced algorithms and models used in the world today. It is simply awesome!
par Christopher A•
1 oct. 2016
The best course in the specialization thus far. Very rich and wide ranging, perfect for the motivated part-time learner who wants to be challenged and have ample reason to revisit the material. I only wish this course had been longer, perhaps shortening the classification course to make room.
par Krishna K•
20 avr. 2018
This is very nice and interesting course. It gives practical application of machine learning application. I would consider this course as applied machine learning course as it lacks mathematical intuition. Nevertheless, course it great and cover major points in the machine learning field.
par Muhammad H A•
13 août 2016
I used to run into a wall at work trying to train models with recursive partitioning or neural nets because of the long time they took to train for high dimensional data. These clustering techniques are an immense help.
Awesome course, with a brilliant instructor and brilliant assignments.
par Phuong N•
7 févr. 2018
This is very useful course that can help me more to understand and resolve the complicated issue in the real world. I want to thank Coursera e-learning and the Washinton University for created this course that help people in the developing country like me can access the new technical.
par Renato R R•
4 janv. 2018
This course is amazing. I could really work on real world problems. It is a pity that we are not going to have the following courses:
Recommender Systems & Dimensionality Reduction
Machine Learning Capstone: An Intelligent Application with Deep Learning
Thank you Emily and Carlos.
par Liling T•
15 août 2016
Emily Fox did a great job in explaining tough concepts with simple explanation of the components in the formulas!!
It's a little tough to get through the materials though, it's the 4th course in University of Washington's machine learning specialization afterall =)
par Martin R•
12 déc. 2018
I'd bring the last summary video at the beginning (the great summary of all weeks of the course). This would outline the course evolution in advance and give guidance what's ahead. IMHO this would help to not get lost when drill down in a single section.
par Kumiko K•
14 août 2016
This course started off easy, and became challenging in the last 3 weeks. But a lot of details were covered in the slides and also the forum helped deepen my understanding of the material, and I was able to get through the course. I enjoyed the course!
par Sally M•
2 janv. 2017
Great course but hard going at times for those of us without a strong maths background. The assignments took me a long time to complete and I think I'll have to revisit some areas as I become more familiar with them to really get the full benefit.
par Michael B•
12 juil. 2016
Not for the faint of heart but this course does a really good job of explaining clustering (and retrieval) of images and text. It includes several programming assignments which can be tackled with minimal programming experience if one perseveres.
par Vaidas A•
29 mai 2017
This course was great! With good code examples and algorithm applications and also intuition!
It's a shame that we couldn't finish planned courses due to busy schedules of instructors as I was really looking forward to the capstone project!
par Bhavesh G•
12 mai 2020
During this course, I learned many new things like 1-NN, clustering using K-means, Gaussian mixture models etc. I would like to suggest this course to all those who want to learn about machine learning and make a career in data science.
par Aditi R•
25 déc. 2016
This course contain many advance topic which was covered in fast pace by the professor special end lectures. This course contain very important topics of Machine learning could have given more time in explaining things. Thanks professor
par Marcio R•
2 sept. 2016
Following the overall quality of this Specialization, this course was excellent. From the content, to the assesments, material and teachers. This course is a really good starting point to become an expert in Machine Learning techniques.
par Jafed E G•
6 juil. 2019
I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand
par Mohamed A H•
19 juin 2019
A very rich of useful materials course. The instructor has a fantastic explanation ability. The course is pretty organized and the assignments solidifies the understanding of the concepts well.
It was an amazing experience!
par Alfred D•
24 mars 2018
KD trees, LSH along with LDH were some real deep techniques I've learnt and benefitted.
Thanks a ton to Emily and Carlos , you guys are amazing teachers for such a complex subject as ML and the algorithms it consists of .
par Atul A•
25 août 2017
Great course. Different from earlier courses in the Specialization, this course is quite challenging in both theory and practice. However, it is super important, as clustering is all around us in real-world data.
par Samuel d Z•
18 juil. 2017
Brilliant, anyone interested to get proficient in Data Science and Machine Learning need to take this course. It is well structured and although very challenging at times, it is always possible to get the right result.