good

good

Nice

good

good

good

GOOD

good

nice

good

good

GOOD

Good

Good

good

good

Good

Good

good

ok

wow

ok

Ok

.

PRO: Good overview and basic introduction of common machine learning techniques.

CON:

- The final assignment is peer reviewed! I saw no mention of this before purchasing the course. This means you are at the mercy of other students who may have less experience than you and may notbe qualified in grading assignments. Also it may mean you have to wait a long time before you get your certificate. It would be better to implement a Kaggle-style assessment of the models and use that to obtain a score and turn that into a grade. This would be transparent and instantaneous.

Some of the forum answers provided by the teaching staff are half baked and often inconsistent. e.g. they give example code for making a figure and and also a figure. But the figure is obviously not made with the provided code and the code contains typos. This is frustrating and makes learning harder than it should be.

Some of the code in the lab exercises don’t obey good practices. e.g. in every lab the data is normalised before train/test splitting. In the final project there is a comment that this should be done the other way around (and it really should!). Why not do it the right way in all the examples throughout the course?