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다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 36시간 필요

권장: 6 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 아랍어

귀하가 습득할 기술

Linear RegressionRidge RegressionLasso (Statistics)Regression Analysis

다음 전문 분야의 4개 강좌 중 2번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 36시간 필요

권장: 6 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 아랍어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Welcome

5개 동영상 (총 20분), 3 readings
5개의 동영상
What is the course about?3m
Outlining the first half of the course5m
Outlining the second half of the course5m
Assumed background4m
3개의 읽기 자료
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Reading: Software tools you'll need10m
완료하는 데 3시간 필요

Simple Linear Regression

25개 동영상 (총 122분), 5 readings, 2 quizzes
25개의 동영상
Regression fundamentals: data & model8m
Regression fundamentals: the task2m
Regression ML block diagram4m
The simple linear regression model2m
The cost of using a given line6m
Using the fitted line6m
Interpreting the fitted line6m
Defining our least squares optimization objective3m
Finding maxima or minima analytically7m
Maximizing a 1d function: a worked example2m
Finding the max via hill climbing6m
Finding the min via hill descent3m
Choosing stepsize and convergence criteria6m
Gradients: derivatives in multiple dimensions5m
Gradient descent: multidimensional hill descent6m
Computing the gradient of RSS7m
Approach 1: closed-form solution5m
Approach 2: gradient descent7m
Comparing the approaches1m
Influence of high leverage points: exploring the data4m
Influence of high leverage points: removing Center City7m
Influence of high leverage points: removing high-end towns3m
Asymmetric cost functions3m
A brief recap1m
5개의 읽기 자료
Slides presented in this module10m
Optional reading: worked-out example for closed-form solution10m
Optional reading: worked-out example for gradient descent10m
Download notebooks to follow along10m
Reading: Fitting a simple linear regression model on housing data10m
2개 연습문제
Simple Linear Regression14m
Fitting a simple linear regression model on housing data8m
2
완료하는 데 3시간 필요

Multiple Regression

19개 동영상 (총 87분), 5 readings, 3 quizzes
19개의 동영상
Polynomial regression3m
Modeling seasonality8m
Where we see seasonality3m
Regression with general features of 1 input2m
Motivating the use of multiple inputs4m
Defining notation3m
Regression with features of multiple inputs3m
Interpreting the multiple regression fit7m
Rewriting the single observation model in vector notation6m
Rewriting the model for all observations in matrix notation4m
Computing the cost of a D-dimensional curve9m
Computing the gradient of RSS3m
Approach 1: closed-form solution3m
Discussing the closed-form solution4m
Approach 2: gradient descent2m
Feature-by-feature update9m
Algorithmic summary of gradient descent approach4m
A brief recap1m
5개의 읽기 자료
Slides presented in this module10m
Optional reading: review of matrix algebra10m
Reading: Exploring different multiple regression models for house price prediction10m
Numpy tutorial10m
Reading: Implementing gradient descent for multiple regression10m
3개 연습문제
Multiple Regression18m
Exploring different multiple regression models for house price prediction16m
Implementing gradient descent for multiple regression10m
3
완료하는 데 2시간 필요

Assessing Performance

14개 동영상 (총 93분), 2 readings, 2 quizzes
14개의 동영상
What do we mean by "loss"?4m
Training error: assessing loss on the training set7m
Generalization error: what we really want8m
Test error: what we can actually compute4m
Defining overfitting2m
Training/test split1m
Irreducible error and bias6m
Variance and the bias-variance tradeoff6m
Error vs. amount of data6m
Formally defining the 3 sources of error14m
Formally deriving why 3 sources of error20m
Training/validation/test split for model selection, fitting, and assessment7m
A brief recap1m
2개의 읽기 자료
Slides presented in this module10m
Reading: Exploring the bias-variance tradeoff10m
2개 연습문제
Assessing Performance26m
Exploring the bias-variance tradeoff8m
4
완료하는 데 3시간 필요

Ridge Regression

16개 동영상 (총 85분), 5 readings, 3 quizzes
16개의 동영상
Overfitting demo7m
Overfitting for more general multiple regression models3m
Balancing fit and magnitude of coefficients7m
The resulting ridge objective and its extreme solutions5m
How ridge regression balances bias and variance1m
Ridge regression demo9m
The ridge coefficient path4m
Computing the gradient of the ridge objective5m
Approach 1: closed-form solution6m
Discussing the closed-form solution5m
Approach 2: gradient descent9m
Selecting tuning parameters via cross validation3m
K-fold cross validation5m
How to handle the intercept6m
A brief recap1m
5개의 읽기 자료
Slides presented in this module10m
Download the notebook and follow along10m
Download the notebook and follow along10m
Reading: Observing effects of L2 penalty in polynomial regression10m
Reading: Implementing ridge regression via gradient descent10m
3개 연습문제
Ridge Regression18m
Observing effects of L2 penalty in polynomial regression14m
Implementing ridge regression via gradient descent16m
4.8
820개의 리뷰Chevron Right

42%

이 강좌를 수료한 후 새로운 경력 시작하기

42%

이 강좌를 통해 확실한 경력상 이점 얻기

19%

급여 인상 또는 승진하기

Machine Learning: Regression의 최상위 리뷰

대학: PDMar 17th 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!

대학: CMJan 27th 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!

강사

Avatar

Emily Fox

Amazon Professor of Machine Learning
Statistics
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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering

워싱턴 대학교 정보

Founded in 1861, the University of Washington is one of the oldest state-supported institutions of higher education on the West Coast and is one of the preeminent research universities in the world....

기계 학습 전문 분야 정보

This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval. You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data....
기계 학습

자주 묻는 질문

  • 강좌에 등록하면 바로 모든 비디오, 테스트 및 프로그래밍 과제(해당하는 경우)에 접근할 수 있습니다. 상호 첨삭 과제는 이 세션이 시작된 경우에만 제출하고 검토할 수 있습니다. 강좌를 구매하지 않고 살펴보기만 하면 특정 과제에 접근하지 못할 수 있습니다.

  • 강좌를 등록하면 전문 분야의 모든 강좌에 접근할 수 있고 강좌를 완료하면 수료증을 취득할 수 있습니다. 전자 수료증이 성취도 페이지에 추가되며 해당 페이지에서 수료증을 인쇄하거나 LinkedIn 프로필에 수료증을 추가할 수 있습니다. 강좌 내용만 읽고 살펴보려면 해당 강좌를 무료로 청강할 수 있습니다.

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