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Voltar para Machine Learning: Regression

Comentários e feedback de alunos de Machine Learning: Regression da instituição Universidade de Washington

5,480 classificações

Sobre o 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....

Melhores avaliações


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!


4 de mai 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

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901 — 925 de 984 Avaliações para o Machine Learning: Regression

por Kunal F

26 de abr de 2020

Too much theory intensive. Should have more practical approaches

por 陈佳艺

30 de abr de 2017

Maybe because I am a graduate student,it seems a little lengthy.

por Xiaofeng H

4 de out de 2016

Hope can recommend some reading materials for some theory parts.

por Farmer

28 de jun de 2018

Very interesting course, but the assignment is a bit too easy.

por Srinivas C

12 de ago de 2018

This course provided deep insights on regression concepts

por Markus M

10 de fev de 2016

Good structure, but maybe a bit too basic and slow pace.

por M.sakif m

8 de jan de 2016

Very thorough and challenging class.Highly recommended.

por Vinay V

28 de jan de 2016

This course is so well structured and the is awesome

por Yegwende V T

8 de fev de 2016

Learn more about linear regression, ridge and so on.

por Mohinish N

22 de mar de 2018

Gives good abstraction of underlying algorithm.

por Rushikesh M N

19 de nov de 2019

Detailed derivation, Loved the way they teach.

por João S

7 de jan de 2016

Nioce course. Compreensive notes and nice (&fu

por Andrew G L

4 de ago de 2017

Great course to get started with regression.

por rajeev r

26 de jan de 2020

Nice introductory ML concepts to star with.

por Shaurya s

1 de jan de 2016

Excellent course except the last week :)

por mohammed T

12 de mar de 2018

i wish that you have used scikit learn

por Pier L L

20 de set de 2016

Very good course. I really liked it.

por Aman G

24 de set de 2018

Don't bug me regarding the review.

por gaozhipeng

12 de fev de 2016

Nice course! Thank you very much ~

por Paul M

22 de dez de 2017

Excellent overview. Great slides

por Michael L

18 de mar de 2017

Far too math, much less practice

por Shashidhar Y

28 de fev de 2019

Good interactive courses.

por egonigilist

17 de ago de 2017

several errors in exams

por Jeyaprabu

4 de mar de 2016

detailed but slower...

por Gaurav S

30 de dez de 2015

Good and Insightful