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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

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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


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 :)


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 201~225

교육 기관: Thierry Y

2017년 11월 12일

Great material, easy to follow, and nice examples around sushis :)

교육 기관: Christian R

2017년 9월 11일

The visualizations provide deeper understanding in the algorithms.

교육 기관: Luis M

2017년 1월 28일

Lots of practical tips, some applicabe not only to Classification.

교육 기관: Yoshifumi S

2016년 5월 8일

As always in this specialization, tough course but so practical !!

교육 기관: Japneet S C

2018년 2월 5일

Course is very good. Concepts are explained in a very simple way.

교육 기관: dragonet

2016년 3월 24일

thank you every much, every helpful! ~i will repeat several time~

교육 기관: Mark W

2017년 5월 6일

Fantastic Lecturers and very interesting and informative course

교육 기관: D D

2016년 10월 16일

Nice videos. Learned a lot. Also videos good for future review.

교육 기관: Eric N

2020년 10월 11일

Excellent online teaching with clear and concise explanations!

교육 기관: Parab N S

2019년 10월 12일

Excellent course on Classification by University of Washington

교육 기관: Mohd A

2016년 8월 14일

Learning is fun when you have professors like Carlos Guestrin.

교육 기관: Ali A

2017년 9월 4일

the course material is great but the assignments are not good

교육 기관: clara c

2016년 6월 11일

This course was great! I really enjoyed it and learned a lot.

교육 기관: Yufeng X

2019년 6월 14일

The lecture is super. The exams could be more challenging-:)

교육 기관: Sarah W

2017년 9월 24일

Great course! Learned so much! So excited to use this stuff!

교육 기관: Tony T

2016년 11월 19일

funny and enthusiastic lecturer make a dry subject more fun.

교육 기관: Simbarashe M

2020년 9월 24일

l know a knew way to train the models taught in this course

교육 기관: Isaac B

2016년 11월 20일

Excellent course. Practical understanding of classification

교육 기관: Ali A

2016년 3월 21일

So far it is a mazing. I will rate at the end of the course

교육 기관: Kartik W

2020년 9월 19일

A must do course for all the machine learning enthusiasts.

교육 기관: Koen O

2017년 4월 14일

Excellent course for learning the basics on classification

교육 기관: Chao L

2017년 3월 31일

Nicely formatted. And it's quite intuitive and practical.

교육 기관: Patrick P

2016년 11월 28일

Very good and and informative to start with this subject.

교육 기관: vacous

2017년 8월 3일

very nice material covering the basic of classification.

교육 기관: Xuan Q

2017년 2월 13일

Super useful and a bit of challenging! Really enjoy it.