Learner Reviews & Feedback for Practical Predictive Analytics: Models and Methods by University of Washington

The lessons are sometimes completely disconected from the graded assignments. There were some graded assignements that dealt with things I have never heard about and I completed it without even looking the lessons videos. Some of the lessons are disapointing of the lack of assistance to the required software/code to be used. In such a way that the concept worked is very simple, but if you have no experience on the software or code you can have a hard time to complete the assignements with irritating details which are not explained at all in the lessons. The lessons serves more as a guide to what you should search in google and learn through other source of information. I did not expected such poor course from a paid one; I have doen free courses way better than this course. Don´t pay or this course, find some other course free or other paid course with better reviews.

Do not like the slides and the way it is explained. Compared with other ML courses on cousera, this one makes me feel that it is more like a handbook/dictionary instead of a tutorial to teach students. If you already know it, it would help you refresh the mind. Otherwise, you might find it is just to show off how how complex and mysterious is the data science.

I can feel Prof. Howe tried to cover as much as possible and to build a foundation for both practicing as well as further study on the topics. However, I do feel it is not patient enough to give a detailed yet easy-to-follow explanation for some of the topics, and I had to do quite some self-readings to close the gap. I think it will be helpful if the course can provide some reading materials on how some of the formulas are derived (e.g. gradient descent, logistic regression etc.) as a supplement.

Fantastic course! Excellent conceptual teaching for people who already know the subject but need some more clarity on how to approach statistical tests and machine learning.

I enjoy this course. The delivery and the course topics were very interesting. I learnt a lot and peer reviewing other people assignments is a great learning opportunity .

The topic the professor covers are awesome. Going from statistics to machine learning is something very awesome about this course

Nive that the course covered a broad range of topics.

And good to get pushed to do some kaggle competition and peer review.

A quick overview of technology terms used for Machine Learning, and gentle introduction into learning through Kaggle.

Too little people participated and long peer review time.

But the course content is good.

Very nice assignments and content. You learn a lot when you complete all assignments.

Professor Bill Howe gives great reactions to when there are typos on the slides!

Hands on practices are very good. learning predictive model was a challenge.

Its Hard! but AWESOME, some much info packed in a few lectures!

Its a great review course. Prior knowledge is necessary

Excellent course with amazing practical exercises!

Excellent thoughts and concepts presented.

great for learner

Great course!

Nice course

Excellent!!

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i love it

A nice and informative course. The only negative side were the problems with the automatic evaluation of the R assignment. In my opinion, the question should have been automatically removed and/or all submittions reevaluated, or all students should have been notified about the need for manual resubmission. As it was, some (like myself) were left with fewer points that they should have received just because they did not check the discussion forums every day (mainly because of other obligations).

Excellent crash course in machine learning and introduction to the kaggle data science competitions. However, the grading system had bugs and was unable to accept two answers as correct making it very frustrating. The grader was finally fixed so next round of this course should be a better experience.

This course covers a lot of material, but unfortunately lacks depth and thorough examples in many areas. It could also use more hands-on activities. Overall, I learned quite a bit and found it was worth the time and effort.