Good

great

Super

good

GOOD

good

good

bes5

good

Good

good

Good

best

nil

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ok

.

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As someone with a science background, I have done a great deal of curve/model fitting. This course seems like it would be a useful introduction to these areas. As with other courses in this series, this course displays some useful shortcuts and streamlined methods for doing this work and the coded examples are useful to keep as go-bys for use in future work. On the downside, this course only covers variations on fitting a straight line to your data so it feels rather basic to be classed as "machine learning", and is simpler than I would have hoped for an intermediate course.

this course was awesome from learning point of view as it was more detailed and required pre beginners knowledge about key concepts to move ahead . i have learned many concepts about machine learning models,

statistics , theory implementation part.

what i most enjoyed was the lab work as it was more detailed and there were plenty of things to learn from .

This course is very great. it focuses mainly on codes and how to get your models trained well with the best results. and for that a prior knowledge of the algorithms and the coding language in addition to the different libraries would be better.

Good, but only if someone has a good amount of prerequisite knowledge of statistics, probability, mathematics and coding in machine learning related libraries. Due to this, to me in the beginning, the course pace felt a bit too fast.

Easy to understand and apply (+). Some code uses deprecated functions/methods. (-) Assignment answers seem to be mixed up (on very few occasions) so one has to randomly try out the correct answer to get 100%. (-) Issues reported.

4/5 simply because not all the lesson Jupyter Notebooks are downloadable--the download links do not work. But the course content is very educational and has a good balance of difficulty enough to challenge you while learning.