Kursteilnehmer-Bewertung und -Feedback für Machine Learning: Classification von University of Washington

The course itself is well structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

This course covers all the core algorithms used in Classification models. If you have a basic understanding of machine learning, this course can help you build your understanding of classification on a deeper level.

Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.

Very good content, very well explained... great course. Classification its a very broad topic but i think this is great introduction.

The hands on where kinda on the easy side... but very interesting.

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality

Extremely clear and informative. Good introduction to ML. I felt the labs could have had us write a little more of our own code, and would have been better to use non-proprietary libraries.

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

Great course! However I put only 4 starts because I would like to see random forests which are not present.