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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

4.7
별점
3,670개의 평가

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 575개 리뷰 중 226~250

교육 기관: Carlos L

•

2016년 6월 10일

The contents are really clear and professors are great!

교육 기관: Freeze F

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2016년 6월 7일

This lecture gave a great start for me into ML . :) :)

교육 기관: Sudip C

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2016년 5월 3일

Very detailed, Liked optional sections also. Loved it.

교육 기관: Tarek M A A

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2022년 2월 15일

Full of useful, good put simple explanation for each

교육 기관: Rodrigo T

•

2017년 12월 30일

Excellent course, i really like the general concepts

교육 기관: susmitha

•

2020년 8월 5일

Very clear and good explanation by both instructors

교육 기관: Dohyoung C

•

2019년 6월 3일

Great ...

I learned quite a lot about classification

교육 기관: Maxwell N M

•

2018년 10월 7일

Great Course!

Teachers are genius and awesome

Thanks

교육 기관: Norberto S

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2016년 10월 9일

Excellent course with lots of practical exercises.

교육 기관: JOSE R

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2017년 11월 18일

Very interesting. It's easy to understand. Thanks

교육 기관: Tuan L H

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2016년 12월 6일

Great course, easy to follow, higly recommended!

교육 기관: Syed A R

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2016년 8월 11일

exceptional course. Carlos did an excellenet job

교육 기관: Mariano

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2020년 4월 4일

very interesting and useful tools for real life

교육 기관: 李紹弘

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2017년 8월 14일

This course provides me the very clear concept.

교육 기관: LIU Y

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2016년 3월 22일

best of the best, theoretically and practically

교육 기관: YILIANG L

•

2018년 8월 22일

The course is good. The materials are amazing!

교육 기관: Trinh N Q

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2018년 1월 28일

Give me a good understanding of Classification

교육 기관: Anurag U

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2017년 1월 16일

Best source to learn classification techniques

교육 기관: Binil K

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2016년 7월 30일

Nice Course, very much helpful and reccomended

교육 기관: ANKIT G

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2020년 3월 21일

Very good programming assignments. Loved it.

교육 기관: Arash A

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2016년 11월 30일

Learned a lot and enjoyed even more. Thanks!

교육 기관: 嵇昊雨

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2017년 4월 25일

Great materials for learning Classification

교육 기관: Kan C Y

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2017년 3월 19일

Really a good course, succinct and concise.

교육 기관: clark.bourne

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2016년 5월 8일

Professional, comprehensive, worth to learn

교육 기관: Steve F S

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2020년 6월 24일

challenging course for any non-math major.

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