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Machine Learning: Classification(으)로 돌아가기

워싱턴 대학교의 Machine Learning: Classification 학습자 리뷰 및 피드백

4.7
별점
3,668개의 평가
605개의 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 574개 리뷰 중 451~475

교육 기관: Dawid L

2017년 3월 20일

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

교육 기관: Thuc D X

2019년 6월 27일

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

교육 기관: Gaurav K J

2018년 5월 1일

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

교육 기관: Justin K

2016년 6월 10일

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

교육 기관: Hao H

2016년 6월 12일

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

교육 기관: 김대성

2021년 3월 23일

Very nice lecture & materials. The only slight negative component this lecture contains is the library used for the programming assignment.

교육 기관: Fangzhe G

2020년 2월 7일

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

교육 기관: Brian B

2016년 4월 22일

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.

교육 기관: Fahad S

2018년 11월 3일

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

교육 기관: Aaron

2020년 7월 3일

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

교육 기관: Alexis C

2016년 9월 29일

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

교육 기관: Kishaan J

2017년 7월 1일

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

교육 기관: Raisa M

2017년 8월 19일

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

교육 기관: Ning A

2016년 9월 16일

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

교육 기관: Yingnan X

2016년 4월 14일

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

교육 기관: Oleg R

2016년 10월 9일

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.

교육 기관: Thrivikrama

2016년 10월 12일

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

교육 기관: Tomasz J

2016년 4월 4일

Great course! However I put only 4 starts because I would like to see random forests which are not present.

교육 기관: Baubak G

2018년 6월 10일

I think the course on boosting could be worked on better. But all in all I really enjoyed this course.

교육 기관: Simon C

2020년 5월 1일

It's still a great course. But I think the quality of the regression one is better than this overall.

교육 기관: Scott A

2021년 7월 19일

Class was inconsistent, it started very detailed and became over-simplified in the later weeks.

교육 기관: Srinivas C

2018년 12월 2일

This course was really good and helped in understanding different techniques in Classification

교육 기관: Sapna A

2021년 2월 2일

The course was awesome, especially with sentimental classification case explanation... Thanks

교육 기관: ZhangBoyu

2018년 7월 20일

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

교육 기관: shashank a

2020년 6월 9일

Overall good, But it seems like same type of questions are repeated in assignment quiz