Chevron Left
Back to Machine Learning: Regression

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

5,507 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


May 4, 2020

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


Mar 16, 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!

Filter by:

901 - 925 of 988 Reviews for Machine Learning: Regression

By Charles P

Apr 8, 2020

Good introduction, very good for basic understanding, but lacks depth.

By Patrick M d F

Jul 5, 2019

Excellent trad-off between theory, algorithims and practical examples

By Nipun G

Apr 21, 2019

Please get rid of SFrame and graphlab. However, professor is awesome!

By Andrej

Nov 13, 2016

Very good course, going through plenty of used regression techniques.

By Kunal F

Apr 26, 2020

Too much theory intensive. Should have more practical approaches

By 陈佳艺

Apr 30, 2017

Maybe because I am a graduate student,it seems a little lengthy.

By Xiaofeng H

Oct 4, 2016

Hope can recommend some reading materials for some theory parts.

By Farmer

Jun 28, 2018

Very interesting course, but the assignment is a bit too easy.

By Srinivas C

Aug 12, 2018

This course provided deep insights on regression concepts

By Markus M

Feb 10, 2016

Good structure, but maybe a bit too basic and slow pace.

By M.sakif m

Jan 8, 2016

Very thorough and challenging class.Highly recommended.

By Vinay V

Jan 28, 2016

This course is so well structured and the is awesome

By Yegwende V T

Feb 8, 2016

Learn more about linear regression, ridge and so on.

By Mohinish N

Mar 22, 2018

Gives good abstraction of underlying algorithm.

By Rushikesh M N

Nov 19, 2019

Detailed derivation, Loved the way they teach.

By João S

Jan 7, 2016

Nioce course. Compreensive notes and nice (&fu

By Andrew G L

Aug 4, 2017

Great course to get started with regression.

By rajeev r

Jan 26, 2020

Nice introductory ML concepts to star with.

By Shaurya s

Jan 1, 2016

Excellent course except the last week :)

By mohammed T

Mar 12, 2018

i wish that you have used scikit learn

By Pier L L

Sep 20, 2016

Very good course. I really liked it.

By Aman G

Sep 24, 2018

Don't bug me regarding the review.

By gaozhipeng

Feb 12, 2016

Nice course! Thank you very much ~

By Paul M

Dec 22, 2017

Excellent overview. Great slides

By Michael L

Mar 18, 2017

Far too math, much less practice