Machine Learning: Regression(으)로 돌아가기

4.8

4,460개의 평가

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838개의 리뷰

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: Virendra S S

•Aug 09, 2018

awesome course.Regression concepts are deeply covered .Be careful doing assignments .assignments are long but they are from scratch you will get to know to how machine learning algorithm actually works .

교육 기관: Anantha P

•Aug 06, 2018

Great course on Regression. This course explains the basic regression algorithms and the math behind these algorithms in a way that is easily understandable. Apart from the explanation, the assignments are also awesome, where you get to try out all the algorithms in the machine learning libraries as well as implement them your own.

교육 기관: Charlie Q

•Aug 11, 2018

Very clear and detailed presentation of concepts and techniques of the traditional regression approach that are most relevant in today's machine learning world. The assignments are well designed and may take some efforts to complete, but they are worth the time as they certainly reinforce the understanding of materials covered in the lectures.

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: Sandeep B S

•Aug 25, 2018

Great content from the professor, learning regression was very interesting

교육 기관: Fakrudeen A A

•Aug 26, 2018

Excellent course and requires some hardwork during weekends but pays off very well. It teaches Liner Regression, regularization, loss fn and k-NN among others - all very important ML concepts.

Thank you to excellent teachers!

교육 기관: 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.

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: Yashaswi P

•Sep 13, 2018

The only hindrance I had is with understanding the problem statements in assignments. It would be better to use a more unambiguous text.

교육 기관: Courage S

•Sep 11, 2018

Emily Foxx's teaching methods in this course are the bomb. She does not give you code hints as Carlos Guestrin would, but rest assured she breaks the concepts down to basic learning blocks and does a pretty neat job at connecting the dots between blocks to present a holistic picture of the course.

I called out her name countless times trying to wade through the programming tasks. Guess that worked for me many times as I imagined her tutoring me in a PhD class and breathing down my neck to meet deadline on pay resit fees (akin to Coursera subscription charges).

Overall, 7-Star Course and Teaching Methods.

교육 기관: gaston F

•Oct 01, 2016

Fine course

교육 기관: George K

•Mar 09, 2016

The professors help understand the concepts from ground up. Seriously recommended course if you want to know how Regression works and all about ridge, lasso and kernel regression.

교육 기관: Biswa D

•Sep 27, 2016

Clearly illustrates the essential concepts to prep for more advanced stuff

교육 기관: Vijai K S

•Dec 07, 2015

It is a very thorough course.

교육 기관: Mahmoud F

•Feb 06, 2017

One of the most awesome courses in Machine Learning, with awesome mentors.

교육 기관: Miao J

•Dec 17, 2015

Great course, which offer you a very practical experience in machine learning!

교육 기관: Dan L

•Dec 28, 2015

I found this an excellent introduction to the topic with a good mix of well-presented material and practical application using the IPython notebooks. I would love to have the course finish with a project where we apply the learned methods to a different data set.

교육 기관: Jennifer H

•Apr 14, 2016

Excellent introduction to regression - filled in a lot of concepts that I needed!

교육 기관: Sander v d O

•Mar 16, 2016

Superb course, very well explained! The best I've taken so far!

You do need to know some Linear Algebra and Python as a prerequisite, but as a result, after hard work, I have now finally developed some true understanding of a wide range of regression algorithms.

Minor downside: i find the activity in the forum quite low, so not to useful in this course.

교육 기관: 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.

교육 기관: Yin X

•Sep 09, 2017

Best course I have had so far on regression at Coursera. Thaaaaaank you Coursera and Washington U!

교육 기관: Chengcheng L

•Dec 28, 2015

I feel I understand regression models better than before. But I still need to read more books on the same topic to actually convert what I learned here to long term memory :)

교육 기관: SATYAM S

•Mar 02, 2016

This course was simply awesome. Professors have explained every concept so well.

교육 기관: Bilkan E

•Oct 16, 2016

Incredible course!

Very good, intuitive and simple introduction to general use machine learning and optimization techniques. I am already employing techniques learned here to my daily work.

교육 기관: Manuel S

•Jun 18, 2016

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