Machine Learning: Regression(으)로 돌아가기

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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....

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!

필터링 기준:

교육 기관: Evaldas B

•Nov 28, 2017

Very good and accurate course about regresion. Not just the basics but a lot of things you can use in real life chalenges.

교육 기관: Syed A u R

•Jan 11, 2016

Exceptional course!. Emily went into great details of the regression algorithms and its application. Thoroughly enjoyed it.

교육 기관: George G

•Oct 10, 2018

The course provided many useful insights on Regression techniques, and provided in depth understanding of the task in hand

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: Devasri L

•Apr 10, 2020

Very helpful course. I sincerely thank Coursera and University of Washington to provide this opportunity to learn.

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.