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Opiniones y comentarios de aprendices correspondientes a Machine Learning: Regression por parte de Universidad de Washington

5,507 calificaciones

Acerca del Curso

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....

Principales reseñas


4 de may. de 2020

Excellent professor. Fundamentals and math are provided as well. Very good notebooks for the’s just that turicreate library that caused some issues, however the course deserves a 5/5


16 de mar. de 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 de 988 revisiones para Machine Learning: Regression

por Sameer C

25 de jun. de 2016

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

por RAUL G & F - L & R E

11 de ene. de 2018

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

por Krishna C

18 de ene. de 2016

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

por shashank a

3 de jun. de 2020

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

por Oleg S

10 de oct. de 2017

...really challenging...

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

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

por Moises V

24 de mar. de 2016

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

por ayshwarya s

5 de feb. de 2019

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

por Varun R

6 de feb. de 2016

Quite a hard course...

But laid great foundations and reduced the dependence on graphlab.

Thanks Emily!

por 林俊凡

8 de dic. de 2015

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

por Bob

6 de feb. de 2017

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

por Farrukh

11 de ene. de 2017

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

por Piyush G

25 de feb. de 2019

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

por Onwumere O B

15 de mar. de 2016

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

por Braden W

13 de ago. de 2018

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

por Morgan M

13 de oct. de 2017

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

por J G

8 de jul. de 2018

It is a good course, it is really challenging learning how to do it from scratch.

por Steve M

17 de nov. de 2016

A good course overall. at time, the programming assignment is somewhat confusing.

por sandeep d

19 de ago. de 2020

nice theoretical , it would be better if you teach the given notebook examples

por Mridul C

20 de jul. de 2020

It would have been better if the coding part was also covered in videos only.


30 de oct. de 2020

좋은 내용, 자세하고 친절한 설명, 적절한 난의도가 좋았습니다. 다만, 일부 모듈이 윈도우10 환경에서 사용하기 어려운 경우가 있습니다.

por J N B P

5 de oct. de 2020

A really good course which covers the complete concepts of regression models

por Saadullah A

23 de jun. de 2016

Great introductory course for regression analysis and very practical indeed!

por Rafał P

11 de oct. de 2020

Turicreate is a bit confusing. Especially, while having Graphlab in videos

por Sourabh S

22 de mar. de 2017

Course covers in depth many topics. Only some issues with using Pandas.

por Aditya S

17 de jul. de 2016

Good to have deep insight into regression and various popular algorithm