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    Back to Machine Learning: Regression

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

    Filled StarFilled StarFilled StarFilled StarFilled Star
    4.8
    stars
    5,578 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 feature...
    ...

    Top reviews

    PD

    Mar 17, 2016

    Filled StarFilled StarFilled StarFilled StarFilled Star

    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!

    KM

    May 5, 2020

    Filled StarFilled StarFilled StarFilled StarFilled Star

    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

    Filter by:

    226 - 250 of 1,000 Reviews for Machine Learning: Regression

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Huynh L D

    •

    Jan 14, 2016

    All the courses in this specializations are very well-made and rigorous. I think all MOOCs, especially techinical ones, should be as well-designed as this or even more.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Fahim K

    •

    Jan 6, 2016

    The course is really helpful. It has started with simple Regression model and gradually build the different advance regression model. Thanks for this wonderful course.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Aditya K

    •

    Aug 15, 2016

    rigorously explained some of the most important algorithms in regression world, also the pros and cons of using certain algorithm for certain conditions. totally worth

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Sahil D

    •

    May 16, 2016

    Good overall theoretical and practical explanation of the material, I was also able to use scikit learn and pandas without any difficulties instead of graphlab create.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By isanco

    •

    Jan 25, 2016

    Great class (really liked the graphical interpretations of Lasso and Ridge optimizations).

    Perhaps some quizzes (and especially assignements) could be more challenging?

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Iñaki D R

    •

    Jul 12, 2020

    Great course, excelent professors & simple yet accurate explanations, always guiding you through the course and through practical implementation of acquired knowledge

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Thomas K A W

    •

    Jan 8, 2018

    Great course! I love the instructors and the thoroughly designed structure of their course. The effort they put into this course certainly shines through every video!

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Jessie J S

    •

    May 12, 2018

    I love this course! It explains more about Regression itself and not just discussing on how to use libraries for it! Very intuitive and informative at the same time!

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Kapil K

    •

    Feb 14, 2017

    its a great course. little bit disappointed from the decision of not continuing Recommended systems and capstone project. PLEASEEEEEE roll out course 5 and 6 as well

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Saheed S

    •

    Sep 19, 2017

    Nice course. I started with this specialization as a beginner. I was very intuitive and great course I would recommend to others people interested in data science.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Santosh G

    •

    Jun 10, 2016

    The Regression course is pretty amazing. Got to learn a lot of cool stuffs. Emily Fox made everything clear. Glad to have taken this course and the specialization.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Hritik K S

    •

    Oct 27, 2019

    Coursera is shaping me in the best version of myself through knowledge and guide. I am always be grateful of god that I found Coursera. My online teaching guru!

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Ian F

    •

    Jun 9, 2017

    Great course - you'll become much more accustomed to Python if you aren't already (I'm an R convert) and really learn the principles behind regression analysis.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Kris D

    •

    Dec 24, 2016

    Covered a lot of the common practical aspects of regression modelling and also covered the calculus derivations for those who are curious. Great course overall.

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    By Hongbing K

    •

    Jan 2, 2016

    Very clear and thorough explanation on regression and implementation details. The closed-form calculation and comparison against gradient descent is excellent.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Emil K

    •

    Jan 14, 2020

    I love how this course goes deep into the math, yet makes it quite approachable even if you have no math skills. Emily is so good at explaining the concepts!

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Bruno V R S

    •

    Aug 27, 2020

    Excelent Course. It not only teaches the ideia behind the topics but it also provides an in-depth view of the algorithms and its parts. Totally recomend it.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Salomon D

    •

    Aug 28, 2018

    Great background through applications of linear regression and explanations that are step by step that allow the understanding and construction of learning.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Marcus V M d S

    •

    Oct 7, 2017

    Thank you for all the effort you put in the exercises and the data. It was a great course! Perhaps you could put references for further study of the topics?

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Melwin J

    •

    Jul 30, 2017

    The best course on regression I have attended so far !!! I really liked the way professor explained the concepts. Has resources on in-depth details as well.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Mantraraj D

    •

    May 5, 2018

    The course should move away from the default graphlab implementation to scikit-learn as the package is outdated and python 2 is about to go out of support

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Girish S

    •

    Dec 19, 2015

    Liked this course, really good assignments which help you to master the concepts thought in the lectures. Thanks a lot for making this available for us.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Tarun G

    •

    Jul 22, 2017

    One of the best courses on Regression. Covers topics in detail with all basics covered. Highly recommended for all analysts/data-scientists out there.

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    By Dennis M

    •

    Apr 25, 2016

    This is a great course, pretty obvious that Emily 1) knows her stuff and 2) put a lot of work into this class to provide an a nice look at regression.

    Filled StarFilled StarFilled StarFilled StarFilled Star

    By Santosh K D

    •

    Jun 5, 2019

    Professor Emily Fox should do a follow up for this course. It was so simple and intuitive to understand. I want to work as a PhD student under her.

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