This course will help us to evaluate and compare the models we have developed in previous courses. So far we have developed techniques for regression and classification, but how low should the error of a classifier be (for example) before we decide that the classifier is "good enough"? Or how do we decide which of two regression algorithms is better?
By the end of this course you will be familiar with diagnostic techniques that allow you to evaluate and compare classifiers, as well as performance measures that can be used in different regression and classification scenarios. We will also study the training/validation/test pipeline, which can be used to ensure that the models you develop will generalize well to new (or "unseen") data.
PT
The course provided a lot of insights into predictive modeling.
NS
Excellent content, but presentation is a bit challenging at times.
Showing: 10 of 10
Peer review system HAS TO CHANGE, IT IS VERY POOR currently - As I type this review, I am waiting for my assignment review for around a weeks time now, no luck in getting any feedback no matter how many threads you make or post in discussion forums like mentioned in FAQs.
This peer review system of having 3 reviews to complete the course has to change to 1 peer review for completion of the course. There is no point in making a learner wait for 7-10 days time without providing any kind of feedback. I put the same comment for Course 2 & I am iterating the same comment/suggestion once again in course 3.
The course provided a lot of insights into predictive modeling.
Insightful. Thank you so much!!
Great course.
Great course
good
Se
Excellent content, but presentation is a bit challenging at times.
A good course about parameters optimization.
Very poor respect the first curse of the specialization