good

good

good

nice

good

nice

N

i

c

e

Nice

Nice

osm

...

ok

ok

。

5

s

T

G

g

G

a

Course is excellent in content. Not heavy in mathematics (altough, I would recommend reading how models are supposed to work), the objectiv eis to have a practical understanding of how machine learning is applied and the important concepts to consider for a succesful model building. The focus is to have hand-on experience with the sklearn library.

I don't grant 5 starts (I hesitated for 4), as the course was designed back in 2018, therefore, you sometimes struggle with legacy libraries. Another issue, is that there are some hiccups when it comes to assignment uploads (for instance, the address of csv files!). As a student, this will make you hesistate and question wether the instructor screwed up with the autograder or not, which IS stressful.

Quiz 4 suddenly became non-forgiving, multiple choice answer have to be answered with 100% certainity to score full point. Quite anti-climatic, considering that previous quizes didn't work like that.

Final assignment is quite challenging, and might make the new student suffer.

I appreciate the instructors and Kevyn Collins for this great course. Now that I have a better picture, I get insights on how to focus my research efforts in sensor research and development.

I had no ML background, although I have the math the models are based on. The material seemed more than week's worth for a couple of weeks. The quizzes make sure you don't miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble-shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.

This course involves lots of concepts and algorithms in machine learning. As it is said by the teacher, for time, effort and aim limitations, this course only involves basic concepts and usage of sci-kit learn. It is a good hand-on course for beginners. Assignments are not so challenging compared with the previous two courses in the same specialization. I just finish assignments by following the module code in the course. I feel like not study as much as I expected through the assignment. I hope assignments can be changed by varieties and difficulties to let students know how a machine learning project is like and how the evaluation works but not simply call the precision/accuracy/recall function and the assignment finishes. Generally, you still learn a lot if you want 'applied machine learning'

I personally enjoyed this course much more than the previous 2 courses in the specialisation. Overall, this course is ambitious and covers a lot of different algorithms. For each algorithm, a brief intuition is provided and we are taught how to code in Python. For this course, I felt that the assignments were a closer fit to the content covered in the videos (unlike the previous courses where the assignments required much more independent learning). However, this course will not provide the mathematical rigour that some learners may expect. Furthermore, the amount of content covered could be a bit overwhelming. It would be useful if the instructor could summarise the different steps we should take when faced with a ML problem, esp. for deciding which algorithm to use (since so many were covered)