About this Course
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다음 전문 분야의 4개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 42시간 필요

권장: 7 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 아랍어

귀하가 습득할 기술

Logistic RegressionStatistical ClassificationClassification AlgorithmsDecision Tree

다음 전문 분야의 4개 강좌 중 3번째 강좌:

100% 온라인

지금 바로 시작해 나만의 일정에 따라 학습을 진행하세요.

유동적 마감일

일정에 따라 마감일을 재설정합니다.

완료하는 데 약 42시간 필요

권장: 7 weeks of study, 5-8 hours/week...

영어

자막: 영어, 한국어, 아랍어

강의 계획 - 이 강좌에서 배울 내용

1
완료하는 데 1시간 필요

Welcome!

8개 동영상 (총 27분), 3 readings
8개의 동영상
Course overview3m
Outline of first half of course5m
Outline of second half of course5m
Assumed background3m
Let's get started!45
3개의 읽기 자료
Important Update regarding the Machine Learning Specialization10m
Slides presented in this module10m
Reading: Software tools you'll need10m
완료하는 데 2시간 필요

Linear Classifiers & Logistic Regression

18개 동영상 (총 78분), 2 readings, 2 quizzes
18개의 동영상
Linear classifier model5m
Effect of coefficient values on decision boundary2m
Using features of the inputs2m
Predicting class probabilities1m
Review of basics of probabilities6m
Review of basics of conditional probabilities8m
Using probabilities in classification2m
Predicting class probabilities with (generalized) linear models5m
The sigmoid (or logistic) link function4m
Logistic regression model5m
Effect of coefficient values on predicted probabilities7m
Overview of learning logistic regression models2m
Encoding categorical inputs4m
Multiclass classification with 1 versus all7m
Recap of logistic regression classifier1m
2개의 읽기 자료
Slides presented in this module10m
Predicting sentiment from product reviews10m
2개 연습문제
Linear Classifiers & Logistic Regression10m
Predicting sentiment from product reviews24m
2
완료하는 데 2시간 필요

Learning Linear Classifiers

18개 동영상 (총 83분), 2 readings, 2 quizzes
18개의 동영상
Finding best linear classifier with gradient ascent3m
Review of gradient ascent6m
Learning algorithm for logistic regression3m
Example of computing derivative for logistic regression5m
Interpreting derivative for logistic regression5m
Summary of gradient ascent for logistic regression2m
Choosing step size5m
Careful with step sizes that are too large4m
Rule of thumb for choosing step size3m
(VERY OPTIONAL) Deriving gradient of logistic regression: Log trick4m
(VERY OPTIONAL) Expressing the log-likelihood3m
(VERY OPTIONAL) Deriving probability y=-1 given x2m
(VERY OPTIONAL) Rewriting the log likelihood into a simpler form8m
(VERY OPTIONAL) Deriving gradient of log likelihood8m
Recap of learning logistic regression classifiers1m
2개의 읽기 자료
Slides presented in this module10m
Implementing logistic regression from scratch10m
2개 연습문제
Learning Linear Classifiers12m
Implementing logistic regression from scratch16m
완료하는 데 2시간 필요

Overfitting & Regularization in Logistic Regression

13개 동영상 (총 66분), 2 readings, 2 quizzes
13개의 동영상
Visualizing overfitting with high-degree polynomial features3m
Overfitting in classifiers leads to overconfident predictions5m
Visualizing overconfident predictions4m
(OPTIONAL) Another perspecting on overfitting in logistic regression8m
Penalizing large coefficients to mitigate overfitting5m
L2 regularized logistic regression4m
Visualizing effect of L2 regularization in logistic regression5m
Learning L2 regularized logistic regression with gradient ascent7m
Sparse logistic regression with L1 regularization7m
Recap of overfitting & regularization in logistic regression58
2개의 읽기 자료
Slides presented in this module10m
Logistic Regression with L2 regularization10m
2개 연습문제
Overfitting & Regularization in Logistic Regression16m
Logistic Regression with L2 regularization16m
3
완료하는 데 2시간 필요

Decision Trees

13개 동영상 (총 47분), 3 readings, 3 quizzes
13개의 동영상
Recursive greedy algorithm4m
Learning a decision stump3m
Selecting best feature to split on6m
When to stop recursing4m
Making predictions with decision trees1m
Multiclass classification with decision trees2m
Threshold splits for continuous inputs6m
(OPTIONAL) Picking the best threshold to split on3m
Visualizing decision boundaries5m
Recap of decision trees56
3개의 읽기 자료
Slides presented in this module10m
Identifying safe loans with decision trees10m
Implementing binary decision trees10m
3개 연습문제
Decision Trees22m
Identifying safe loans with decision trees14m
Implementing binary decision trees14m
4
완료하는 데 2시간 필요

Preventing Overfitting in Decision Trees

8개 동영상 (총 40분), 2 readings, 2 quizzes
8개의 동영상
Early stopping in learning decision trees6m
(OPTIONAL) Motivating pruning8m
(OPTIONAL) Pruning decision trees to avoid overfitting6m
(OPTIONAL) Tree pruning algorithm3m
Recap of overfitting and regularization in decision trees1m
2개의 읽기 자료
Slides presented in this module10m
Decision Trees in Practice10m
2개 연습문제
Preventing Overfitting in Decision Trees22m
Decision Trees in Practice28m
완료하는 데 1시간 필요

Handling Missing Data

6개 동영상 (총 25분), 1 reading, 1 quiz
6개의 동영상
Modifying decision trees to handle missing data4m
Feature split selection with missing data5m
Recap of handling missing data1m
1개의 읽기 자료
Slides presented in this module10m
1개 연습문제
Handling Missing Data14m
4.7
472개의 리뷰Chevron Right

45%

이 강좌를 수료한 후 새로운 경력 시작하기

44%

이 강좌를 통해 확실한 경력상 이점 얻기

13%

급여 인상 또는 승진하기

Machine Learning: Classification의 최상위 리뷰

대학: SSOct 16th 2016

Hats off to the team who put the course together! Prof Guestrin is a great teacher. The course gave me in-depth knowledge regarding classification and the math and intuition behind it. It was fun!

대학: CJJan 25th 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

강사

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Carlos Guestrin

Amazon Professor of Machine Learning
Computer Science and Engineering
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Emily Fox

Amazon Professor of Machine Learning
Statistics

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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