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

4.8
stars
5,538 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 4, 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 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!

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926 - 950 of 993 Reviews for Machine Learning: Regression

By mohammed T

•

Mar 12, 2018

i wish that you have used scikit learn

By Pier L L

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Sep 20, 2016

Very good course. I really liked it.

By Aman G

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Sep 24, 2018

Don't bug me regarding the review.

By gaozhipeng

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Feb 12, 2016

Nice course! Thank you very much ~

By Paul M

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Dec 22, 2017

Excellent overview. Great slides

By Michael L

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Mar 18, 2017

Far too math, much less practice

By Shashidhar Y

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Feb 28, 2019

Good interactive courses.

By egonigilist

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Aug 17, 2017

several errors in exams

By Jeyaprabu

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Mar 4, 2016

detailed but slower...

By Gaurav S

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Dec 30, 2015

Good and Insightful

By Mehul P

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Aug 9, 2017

Nicely explained.

By Sandeep K S

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Jan 25, 2016

excellent course

By Qing W

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Dec 6, 2017

actually good

By James H

•

Nov 12, 2016

Great course

By Abhishek m

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Jan 23, 2021

nice course

By PHILIPPE R

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Jan 26, 2016

Nice course

By NIGAM P

•

Nov 1, 2020

Great Job!

By Rohit K S

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Sep 30, 2020

Nice One!!

By Bruno G E

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Apr 17, 2016

Awesome!

By Sorin S

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May 8, 2016

Great

By pavan k d

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Nov 26, 2021

good

By VIGNESHKUMAR R

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Aug 23, 2019

good

By Irfan S

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Oct 17, 2017

C

By Oliverio J S J

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Jun 8, 2018

This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the otThis course has interesting contents about regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that so much detail is necessary to understand what these algorithms do; more intuitive explanations are missing. On the other hand, as in the previous course, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.her hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.This course has interesting contents about the regression algorithms but sometimes it goes into too many mathematical details and it is easy to get lost. I'm not sure that much detail is necessary to understand what algorithms do, something else is missing to explain them intuitively. On the other hand, as in the previous academic year, the material has not been updated to reflect that the last courses of the specialty have been canceled.

By Terry S

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Jul 18, 2016

This course offers great background instruction on Machine Learning and I would give it 5 stars except for the following:

First, there doesn't seem to be any moderation of the session discussions except for help from other students. This was worth a -2 star penalty. This and the lack of any review of linear algebra and vectorized solutions, I think, is giving some students the impression that they should be coding loops in their functions to build and solve ML models.

Next, I am auditing the course, and this is the first course where I was not able to submit quizzes. Therefore, I can only guess at my solutions. This was worth a -1 star penalty.

UPDATE: not being able to submit quizzes is a "feature" of the new Coursera platform. I never did get an answer from the discussion forums, but I see the same problem in other Coursera courses I am taking.

However, I still think the course is worth taking, so I added back a star. This is the second ML course I have taken. The first was from Stanford ML course which was very specific to implementation in the Octave language. I got a lot more background information from this course, and I think it is well taught. Just wish there were more moderators that were actively watching the discussion list.