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學生對 华盛顿大学 提供的 Machine Learning: Classification 的評價和反饋

4.7
stars
2,949 個評分
486 條評論

課程概述

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)....

熱門審閱

SS

Oct 16, 2016

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!

CJ

Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses

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201 - Machine Learning: Classification 的 225 個評論(共 454 個)

創建者 Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

創建者 Arash A

Dec 01, 2016

Learned a lot and enjoyed even more. Thanks!

創建者 嵇昊雨

Apr 26, 2017

Great materials for learning Classification

創建者 Kan C Y

Mar 19, 2017

Really a good course, succinct and concise.

創建者 clark.bourne

May 09, 2016

Professional, comprehensive, worth to learn

創建者 Md s

Jun 09, 2019

awesome course , have learned lot of stuff

創建者 Fabiano B

Jul 21, 2017

It is a very good course. Congratulations!

創建者 alireza r

May 29, 2017

It is really engaging and well explained.

創建者 Ashley B

Nov 30, 2016

Great course. Material well presented and

創建者 Abhishek T G

Jun 22, 2016

The quizzes can be a bit more challenging

創建者 VITTE

Jul 18, 2018

Very clear and useful course, excellent.

創建者 Hansel G M

Nov 01, 2017

Great course !!! I totally recommend it.

創建者 Aditi R

Oct 20, 2016

Wonderful experience. Prof is very good.

創建者 Manuel I C M

May 30, 2017

One of the best courses i've ever tried

創建者 Garvish

Jun 14, 2017

Great Information and organised course

創建者 Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

創建者 MAO M

May 07, 2019

lots of work. very good for beginners

創建者 Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

創建者 Xue

Dec 15, 2018

Very good lessons on classification.

創建者 Aayush A

Jul 16, 2018

very good course for classification.

創建者 Colin B

Apr 09, 2017

Really interesting course, as usual.

創建者 Jialie ( Y

Feb 08, 2019

It is really useful and up to date.

創建者 Sean L

Aug 31, 2016

wonderful course for beginner of ML

創建者 Alessandro B

Oct 31, 2017

nice, clear engaging ...and useful

創建者 易灿

Nov 28, 2016

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!