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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

4.7
별점
3,640개의 평가
601개의 리뷰

강좌 소개

Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We've also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended)....

최상위 리뷰

SM
2020년 6월 14일

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

SS
2016년 10월 15일

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!

필터링 기준:

Machine Learning: Classification의 570개 리뷰 중 251~275

교육 기관: Ashley B

2016년 11월 29일

Great course. Material well presented and

교육 기관: Abhishek G

2016년 6월 22일

The quizzes can be a bit more challenging

교육 기관: VITTE

2018년 7월 18일

Very clear and useful course, excellent.

교육 기관: Hansel G M

2017년 11월 1일

Great course !!! I totally recommend it.

교육 기관: Aditi R

2016년 10월 20일

Wonderful experience. Prof is very good.

교육 기관: Madhusudhan r D

2020년 6월 27일

Ex ordinary subject with nice concepts.

교육 기관: Israel C

2017년 5월 30일

One of the best courses i've ever tried

교육 기관: Garvish

2017년 6월 14일

Great Information and organised course

교육 기관: Lei Q

2016년 3월 16일

Excellent theory and practice(coding)!

교육 기관: David P

2020년 6월 27일

A great course and a great teacher!!!

교육 기관: MAO M

2019년 5월 6일

lots of work. very good for beginners

교육 기관: Dhruvil S

2018년 1월 10일

Nice Course Clears a lot of concepts.

교육 기관: Xue

2018년 12월 14일

Very good lessons on classification.

교육 기관: Aayush A

2018년 7월 16일

very good course for classification.

교육 기관: Colin B

2017년 4월 9일

Really interesting course, as usual.

교육 기관: Jialie ( Y

2019년 2월 8일

It is really useful and up to date.

교육 기관: Sean L

2016년 8월 31일

wonderful course for beginner of ML

교육 기관: Cosmos D I

2020년 3월 29일

This course is very informational!

교육 기관: Alessandro B

2017년 10월 31일

nice, clear engaging ...and useful

교육 기관: 易灿

2016년 11월 28일

课程很生动,讲的很详细,真心谢谢导师!希望能在算法后面多提供点资料!

교육 기관: Henry H

2016년 11월 17일

Very clear and easy to understand.

교육 기관: Albert V d M

2016년 3월 8일

Very instructive, you learn a lot.

교육 기관: Angel S

2016년 3월 8일

Awesome. Waiting for the next one.

교육 기관: Jing

2017년 8월 14일

Better than the regression course

교육 기관: Rishabh J

2016년 12월 19일

Amazing course, Amazing teaching.