Learner Reviews & Feedback for Machine Learning Foundations: A Case Study Approach by University of Washington

Focused too much on graphlab as opposed to the ML. If the course was titled ML with GraphLab I wouldn't mind (and wouldn't have signed up). The gaffs are kind of charming but really I would expect some of the videos to have had another take or two.

It is more a course in using the tools designed by the teachers than machine learning. It might do something for a less experienced user in programming, but I didn't find it much use. The overview of Machine Learning tasks isn't bad.

on one hand good... on other hand annoying ( mixing graphlab and turicreate... shitty wording of the assignment task, info added as side note which was vital for the assignments...etc.) The curse material would need a refresh.

SFrame data do not support by an updated version of the Python, so student won't able to finish their assignments. So instructor need to update the materials and database which is supported by a new version of Python

The Assignments and Quiz questions are hard to read and comprehend.

Although individually the course presentations are ok, overall this course isn't a very relevant or coherent introduction to Machine Learning.

Feel like I have been duped in a way. No capstone project and you are pretty much forced to use Turi Create (proprietary/black-box version of pandas), which I found incredibly hard to install and use.

Content is outdated and should be revamp, the library use in this course is only for python 2.6 which is legacy and should be updated to latest python version using skicit learn instead of graphlab.

The course uses proprietary tools for machine learning and data manipulation, making it effectively useless! However, the material on describing the machine learning algorithms were excellent!

The instruction conceptually is fine, but I really disliked dealing with setting up Graph Lab Create and SFrames when we could have instead been using more commonly used open source software.

If someone is looking for ML foundations and what is ML, they can choose this course. This is very basic course and i feel should be excluded from the ML specialization.

One would learn a thing or two, but the course is very sparse compared to other machine learning courses, and I didn't feel that it was worth the time and the cost.

I do not like this course being tied to a commercial product. In my opinion it should be using an open source python library and not focusing on the Dato product.

The framework is ok, but it would be more insight on the functions would be much more amplifying the learning process.

Good working examples, though!

it's rather a course on using API of proprietary software with very very basic background on the actual math underneath

I didn't like the python environment, I thought it will be more like Ng's course. Nice explanations, but for amateurs.

Due to many technical issues with GraphLab lib I have to reduce acitivity in this curse for only video viewing :(.

Course videos are outdated and requires time to investigate and research. This causes wasted time and effort.

Very, very high altitude introduction presented in a seemingly confused way with a lot of product placement.

TBH this class's experience is not good. The lecturers seem unprepared and they talk very repetitively.

Not given details like what is a vector? people from non technical backgrounds will have tough time

Very good course material. However, Graphlab is no longer free to use for commercial purpose.

This course is to much about graphlab and not enough about the mechanics of machine learning.

The subjectes are not learnt deeply and precisely. Too summarized and vague!

As a non programer, or mathematician, this course is too hard to follow.

They Don't tech the mathematics behind the machine learning.