Mar 17, 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!
Jan 27, 2016
I really like the top-down approach of this specialization. The iPython code assignments are very well structured. They are presented in a step-by-step manner while still being challenging and fun!
교육 기관: LAVSEN D•
Jul 30, 2016
A very good introduction to Machine Learning: Regression, covering the wide range of topics and explanations in lucid way.
교육 기관: Sanjeev B•
Jan 10, 2016
Great instructors! Wish the problem sets were tougher and required more deeper thinking and choice of techniques to apply.
교육 기관: Rajesh V•
Jan 30, 2017
This course has a very detailed explanation of regression and quizzes which evaluates your understanding of the material.
교육 기관: Jeyanthi T•
Aug 12, 2018
Very Informative and Technical Course...But lot of Mathematical derivations were too long. But very patiently explained.
교육 기관: venkatpullela•
Oct 26, 2016
The course is really good. The quizzes and support is really bad as they slow you down and distract with useless issues.
교육 기관: Renato R S•
Feb 19, 2016
A very well designed course. I would recommend to anyone with serious goals on learning regression and machine learning.
교육 기관: Min K•
Sep 14, 2017
Thank you very much to Instructor "Emily and Carlos" for such an excellent and very informative course on regression :)
교육 기관: abhay k•
Sep 13, 2019
What I was trying to get at my starting stage in ML for last 2 months, this course given in 2 weeks.
Thank you coursera
교육 기관: Oscar S•
May 16, 2019
Step by Step about Regression explained well and easy to understand. Mandatory course for every data science begginer.
교육 기관: Kishaan J•
May 30, 2017
Talks about each and every nitty-gritty details of the different types of Regression algorithms that are in use today!
교육 기관: Rubén S F•
Feb 07, 2016
Great course which covers most of regression topics and important thigns such as lasso regression or ridge regression.
교육 기관: Matthias B•
Jan 03, 2016
Great Course, well structured and following a clear path. Would enjoy some more of the optional technical backgrounds!
교육 기관: Barnett F•
Sep 06, 2016
Bingo course, I learned two years ago ,but I just know the concepts, do not know how to code it ,now this course,,,,,
교육 기관: Rahul M•
Feb 27, 2016
It is an awesome Course For Beginners. But I wanted it to be in R since it is more easier to implement things in R.
교육 기관: Jonathan L•
Jan 15, 2016
Visualization of ridge regression and lasso solution path in week 5 is worth the cost of the entire specialization.
교육 기관: Deepak K S•
Nov 18, 2016
Great Course! Complex things explained in simple ways. Challenging Assignments helped in reinforcing the concepts.
교육 기관: Maxwell N M•
Apr 07, 2016
Lasso is very cool for dimension reduction i discover another algorithm powerfull than Personal Component Analysis
교육 기관: Jim J J•
Nov 01, 2018
Great course and well explained. Need to invest time if you want to rally get benefit out of the content covered.
교육 기관: Chokdee S•
Apr 16, 2017
This is one of my favorite courses for ML, The best course for learning regression stuffs ever. I really love it.
교육 기관: kripa s•
Mar 25, 2019
I must say it was great learning experiance. Everything releted to ML regression has been covered so eloquently.
교육 기관: Marcus C•
Feb 08, 2016
great in depth course on regression. I really enjoyed the implementations of different algorithms all by myself.
교육 기관: Mr. J•
Jan 09, 2020
I am giving 5 stars. Visualization of regularization is illuminating. The programming assignments are useful.
교육 기관: Sushil P B•
Sep 08, 2016
Well organised. In depth optional lectures help you learn more about the theoretical foundations. Recommended.
교육 기관: Gilles D•
Jun 01, 2016
Very good course, will teach you a lot about regression and it will become second nature doing it on your own.
교육 기관: Ashutosh A•
Feb 09, 2016
Nice illustrations and concepts are explained in clear & concise way through real life examples and data sets.