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Learner Reviews & Feedback for Machine Learning: Regression by University of Washington

4.8
stars
5,575 ratings

About the Course

Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression. In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets. Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python....

Top reviews

KM

May 5, 2020

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the assignments...it’s just that turicreate library that caused some issues, however the course deserves a 5/5

PD

Mar 17, 2016

I really enjoyed all the concepts and implementations I did along this course....except during the Lasso module. I found this module harder than the others but very interesting as well. Great course!

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By Saeed M

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Sep 22, 2017

great!

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Jan 29, 2017

great!

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May 24, 2016

good!!

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Mar 7, 2016

Super!

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Jan 11, 2016

Thanks

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Aug 21, 2022

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Oct 20, 2020

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May 31, 2020

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Aug 4, 2019

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Feb 20, 2016

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Oct 23, 2024

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Oct 5, 2023

cool

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Mar 20, 2023

GOOD

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Sep 20, 2020

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Sep 1, 2020

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Aug 16, 2020

nice

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Jul 19, 2020

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May 16, 2020

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May 11, 2019

nice

By 王曾

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Sep 26, 2017

good