Chevron Left
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 476~500

교육 기관: shashank a

2020년 6월 9일

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

교육 기관: Rattaphon H

2016년 8월 13일

The questions are hard to understand and ambiguous though their answers are easy.

교육 기관: Bruno G E

2016년 4월 17일

Lack some of classical classification algorithms like SVM and Neural Netwroks.

교육 기관: Jacob M L

2016년 6월 24일

Very approachable material, given the diversity of classification algorithms.

교육 기관: hiram y s

2020년 4월 26일

Very well explained and with careful guidance through the programming steps.

교육 기관: Luiz C

2018년 6월 7일

Clear, good engaging videos, good quality/complexity balance of exercises

교육 기관: Zebin W

2016년 8월 24일

It covers many aspects in clustering and the assignments are very helpful

교육 기관: Luis d l O

2016년 6월 22일

Very easy to follow and didactic. Very good material in the assignments.

교육 기관: Sander v d O

2016년 5월 9일

Simply a great course. Good intro to machine learning classifiation.

교육 기관: Franklin W

2017년 5월 4일

Great beginner/advanced course for Machine Learning Classification!

교육 기관: Pascal U E

2016년 3월 7일

Take you too long to come back, but the content is great. Good job

교육 기관: Michael B

2016년 9월 4일

Good survey of the material, but assignments are superficial.

교육 기관: vardan l

2018년 1월 26일

Some instructions in programming assignments are not clear.

교육 기관: Charan S

2017년 7월 30일

Very nice course, detailed explanations and visualizations.

교육 기관: Sahil M

2018년 7월 10일

Was a good course with some in-depth topics covered!

교육 기관: Jiancheng

2016년 3월 20일

good course but too much easy, can be a good review.

교육 기관: Hanqiao L

2016년 8월 9일

Need more content for SVM and Random Forest

교육 기관: Alejandro T

2017년 9월 9일

It's a really good course, really liked it

교육 기관: Mohit G

2019년 2월 2일

Good, insightful but repetitive coding.

교육 기관: Sah-moo K

2016년 4월 3일

Decision trees and boosting were great.

교육 기관: Chitrank G

2020년 5월 10일

The course is excellent for beginners.

교육 기관: Gareth W J

2019년 8월 26일

A good course to teach the key points.

교육 기관: Hexuan Z

2016년 10월 6일

could be more challengable homework!!

교육 기관: Vladislav V

2016년 5월 13일

It feels like it lacks certain depth.

교육 기관: S G

2020년 5월 22일

Course material can be much better