Learner Reviews & Feedback for Machine Learning: Regression by University of Washington

Far too math, much less practice

Good interactive courses.

several errors in exams

detailed but slower...

Good and Insightful

Nicely explained.

excellent course

actually good

Great course

nice course

Nice course

Great Job!

Nice One!!

Awesome!

Great

good

C

This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the otThis course has interesting contents about regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that so much detail is necessary to understand what these algorithms do; more intuitive explanations are missing. On the other hand, as in the previous course, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.her hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.

This course offers great background instruction on Machine Learning and I would give it 5 stars except for the following:

First, there doesn't seem to be any moderation of the session discussions except for help from other students. This was worth a -2 star penalty. This and the lack of any review of linear algebra and vectorized solutions, I think, is giving some students the impression that they should be coding loops in their functions to build and solve ML models.

Next, I am auditing the course, and this is the first course where I was not able to submit quizzes. Therefore, I can only guess at my solutions. This was worth a -1 star penalty.

UPDATE: not being able to submit quizzes is a "feature" of the new Coursera platform. I never did get an answer from the discussion forums, but I see the same problem in other Coursera courses I am taking.

However, I still think the course is worth taking, so I added back a star. This is the second ML course I have taken. The first was from Stanford ML course which was very specific to implementation in the Octave language. I got a lot more background information from this course, and I think it is well taught. Just wish there were more moderators that were actively watching the discussion list.

The program is well structured, the lessons are interesting and the hands on nice. However, the instructor should really consider to update their material to python 3 + turicreate. Python 2 is reaching EOL in 2020 and should be avoided for teaching/training. I did most of my notebooks with python 3 and turicreate, it is really worth the effort to update the material. The tests are ok, but some looked somewhat buggy (as reported in the forum by many users) and could use a revision

Good topics and well enough explained, I really did learn a bit. But getting through the course is torture if you are using Sklearn (rather than using their tool TuriCreate). The Programming Assignments use different data sets (sometimes?) and are troublesome to download. From a purely UX viewpoint, the assignments are wordy/difficult to follow along with at some points (even when the content is not so difficult)

The instructors have put a lot of effort into this course and I really appreciate that but unfortunately, I was hoping that the assignments were more interactive like in the deep learning specialization and the tool used is not required at all in any job I searched for also It's not required to use it. I learned a lot out of this course but please update the tools used in this course

The program assignment's description was written badly and hard to follow

For example: in week 6's assignment, the description doesn't indicate features list but ask students to compute distance between two houses. I could only find out the feature list in provided ipython notebook template for graphlab which I apparently didn't use.

There are parts of the course which I got very very stuck on.. thankfully the forums have people's previous frustrations / questions on there. Reading these helped. Other than that, this course is the most comprehensive look at regression techniques I've taken yet, and I'm thankful that this course is provided.

Assignments instructions are not very clear. Formulas used in assignments are structured differently then formulas in lectures. Too much emphasis on using turicreate. Not practical- companies do not ask for knowledge of turicreate. Companies ask for knowledge of scikit learn, pandas and numpy.