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學生對 华盛顿大学 提供的 Machine Learning: Regression 的評價和反饋

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5,474 個評分
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1,016 條評論

課程概述

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....

熱門審閱

KM

2020年5月4日

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

PD

2016年3月16日

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!

篩選依據：

901 - Machine Learning: Regression 的 925 個評論（共 983 個）

創建者 Xiaofeng H

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2016年10月4日

Hope can recommend some reading materials for some theory parts.

創建者 Farmer

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2018年6月28日

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

創建者 Srinivas C

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2018年8月12日

This course provided deep insights on regression concepts

創建者 Markus M

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2016年2月10日

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

創建者 M.sakif m

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2016年1月8日

Very thorough and challenging class.Highly recommended.

創建者 Vinay V

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2016年1月28日

This course is so well structured and the is awesome

創建者 Yegwende V T

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2016年2月8日

Learn more about linear regression, ridge and so on.

創建者 Mohinish N

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2018年3月22日

Gives good abstraction of underlying algorithm.

創建者 Rushikesh M N

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2019年11月19日

Detailed derivation, Loved the way they teach.

創建者 João S

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2016年1月7日

Nioce course. Compreensive notes and nice (&fu

創建者 Andrew G L

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2017年8月4日

Great course to get started with regression.

創建者 rajeev r

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2020年1月26日

Nice introductory ML concepts to star with.

創建者 Shaurya s

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2016年1月1日

Excellent course except the last week :)

創建者 mohammed T

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2018年3月12日

i wish that you have used scikit learn

創建者 Pier L L

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2016年9月20日

Very good course. I really liked it.

創建者 Aman G

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2018年9月24日

Don't bug me regarding the review.

創建者 gaozhipeng

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2016年2月12日

Nice course! Thank you very much ~

創建者 Paul M

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2017年12月22日

Excellent overview. Great slides

創建者 Michael L

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2017年3月18日

Far too math, much less practice

創建者 Shashidhar Y

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2019年2月28日

Good interactive courses.

創建者 egonigilist

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2017年8月17日

several errors in exams

創建者 Jeyaprabu

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2016年3月4日

detailed but slower...

創建者 Gaurav S

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2015年12月30日

Good and Insightful

創建者 Mehul P

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2017年8月9日

Nicely explained.

創建者 Sandeep K S

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2016年1月25日

excellent course

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