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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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876 - 900 of 998 Reviews for Machine Learning: Regression

By Suneet T

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Feb 7, 2016

Excellent course to take a deep dive into Regression concepts. Could have been better if the hands on part would have been in R - Programming as well.

By Thakur S S

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Nov 14, 2017

Amazing course, with focus on both theory and application part.

Only problem was the use of GraphLab, would have been lot better if pandas was used

By ANGELICA D C

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Sep 10, 2020

Fue un buen curso, pero noté que a veces cambiaban las fórmulas y no explicaban el porqué. Eso me causó mucha confusión y algo de tiempo perdido.

By Tùng N

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Dec 3, 2015

like it so far, after one week

i like the way they let us code the procedures ourselves.

expect it to level up in the upcoming weeks and classes

By James Q

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Apr 14, 2018

Excellent materials. I don't agree with some of the programming principals, but the ML stuff is spot on and I'm using these lessons daily.

By Ayush S

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Sep 2, 2016

Excellent series of courses. Before this was confused what was my interest in Computer Science, now I've found Machine Learning, perfect.

By Kirill D

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Feb 8, 2016

I think you should make update process of Graphlab more intuitive, this was the only problem I have faced during this wonderful course!

By Diego N

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Jan 31, 2016

Better deep understanding of common machine learning concepts. Still learn some different things than those exposed on andrew ng course

By Amirhossein S

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Jan 13, 2019

Well, I think Carlos teaches way more enthusiastically and energetically than Emily! But I did enjoy my course on this specialization.

By Baubak G

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May 23, 2018

I think the forum activity is a bit low, and I think in some cases the things are overly describes whereas in others it goes too fast.

By Sameer C

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Jun 25, 2016

Overall, the course was really good. But, it would be great if the concept of co-ordinate descent was explained much more clearly.

By RAUL G & F - L & R E

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Jan 11, 2018

Great course - but the exercise and exams are challenging - which is good if you have the programming experience. One really

By Krishna C

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Jan 18, 2016

Its a great course.Please add a module about how to find the significant variables after using all these technologies.

By shashank a

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Jun 3, 2020

Good but needs to updated according to python3, for eq:- print function need brackets in python3 but not python2

By Oleg S

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Oct 10, 2017

...really challenging...

...have to be a real statistician and pythonist...

...need time to absorb new skills...

By Moises V

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Mar 23, 2016

This course is well structured. It covered a good parts of details I was missing on my machine learning path.

By ayshwarya s

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Feb 5, 2019

Well taught !!Could have been better if practical teaching was more !!I mean teaching via coding was more:)

By Varun R

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Feb 6, 2016

Quite a hard course...

But laid great foundations and reduced the dependence on graphlab.

Thanks Emily!

By 林俊凡

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Dec 8, 2015

Good course! Teachers are perfect and knowledge is overall, but the exercise need some improvement.

By Bob

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Feb 6, 2017

Great course. Can only be better if we were taught in the industry standard libraries (fe. SciPy)

By Farrukh N A

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Jan 11, 2017

Overall its a good course on Regression, although its more driven toward mathematics and statics.

By Piyush G

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Feb 25, 2019

The programming assignments were tough ! but the course covers the content very effectively..

By Okechukwu B O

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Mar 15, 2016

The course is really well explained and skills obtained are quite valuable in the labor market

By Braden W

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Aug 13, 2018

Great, difficult course. The Graphlab vs scikit thing is the only reason I dock it a star.

By Morgan M

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Oct 13, 2017

Good, well structured. Content can get a bit dense at times, but good to be challenged!