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

4.8
stars
5,567 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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501 - 525 of 998 Reviews for Machine Learning: Regression

By VANAJAKUMARI D

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May 6, 2024

very useful to improve my skills in new technology

By Veer A S

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Mar 21, 2018

Excellent course to learn about Regression models.

By Deleted A

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Aug 1, 2016

Lovely lectures with easy to follow up mathematics

By Jesus B R F

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Aug 10, 2020

Excellent material and really clear presentations

By Abhishek N S

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Apr 16, 2020

Emily fox is an great instructor for this course.

By Adedeji A

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Feb 16, 2017

Excellent course, you'll get value for your money

By Antonio C d S P

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Nov 13, 2016

Great ! I'm an IT guy learning a lot about math !

By Manuel S

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Jun 18, 2016

Excellent!! A great option to be on the ML track

By Giorgi G

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Dec 24, 2015

Very good course. I'd love to be course mentor :)

By Eric H

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May 21, 2022

Great to know about Machine Learning: Regression

By Smrithi P P

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May 28, 2020

This course was very useful to my future studies

By Giampiero M

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Jun 14, 2019

great course, with more relevant technical infos

By roopam k

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Jul 25, 2017

excellent course. good pace and good assignments

By Tuhin S

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Sep 15, 2016

One of the best courses in Regression Modelling.

By UMAR T

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Mar 12, 2020

Excellent machine learning course on regression

By Nihar K

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Dec 26, 2019

It was very nice and great learning experience.

By Oliver W

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Jul 19, 2016

Very tough course, but I enjoyed the challenge.

By LIU Y

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Mar 22, 2016

best of the best, theoretically and practically

By songwei

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Jan 15, 2016

cool, really great class about Regression model

By Suyash C

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Jun 3, 2020

Courses like these make me want to live longer

By Dat Q D

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Sep 28, 2017

Good course with balance theory and exercises.

By Muhammad A

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Apr 1, 2017

Absolute loved the course. Highly recommended.

By vamsi k

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Sep 21, 2016

very useful to understand the difficult topics

By Rodolfo S

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Jun 7, 2016

It's a really good course. I congratulate you.

By Sumit K

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May 5, 2020

I exprienced many good things in this course.