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Meaningful Predictive Modeling

Python Data Products for Predictive Analytics,

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.
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Rating 3.6★ based on 3 ratings
Length 5 weeks
Starts Jun 26 (47 weeks ago)
Cost $49
From University of California San Diego via Coursera
Instructors Julian McAuley, Ilkay Altintas
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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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.

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Rating 3.6★ based on 3 ratings
Length 5 weeks
Starts Jun 26 (47 weeks ago)
Cost $49
From University of California San Diego via Coursera
Instructors Julian McAuley, Ilkay Altintas
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science
Tags Data Science Data Analysis

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