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学生对 University of Washington 提供的 Machine Learning: Classification 的评价和反馈

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3,737 个评分

课程概述

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)....

热门审阅

IF

Jul 17, 2017

Good overview of classification. The python was easier in this section than previous sections (although maybe I'm just better at it by this point.) The topics were still as informative though!

KL

Jun 23, 2017

Great course. I learned a lot about Classification theories as well as practical issues. The assignments are very informative providing complimentary understanding to the lectures.

筛选依据：

251 - Machine Learning: Classification 的 275 个评论（共 589 个）

创建者 Kan C Y

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Mar 18, 2017

Really a good course, succinct and concise.

创建者 clark.bourne

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May 8, 2016

Professional, comprehensive, worth to learn

创建者 Steve F S

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Jun 23, 2020

challenging course for any non-math major.

创建者 Md s

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Jun 9, 2019

awesome course , have learned lot of stuff

创建者 Fabiano B

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Jul 21, 2017

It is a very good course. Congratulations!

创建者 PAVITHRA B

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Sep 29, 2020

HIGHLY INFORMATIVE AND CHALLENGING COURSE

创建者 Alireza R

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May 29, 2017

It is really engaging and well explained.

创建者 Ashley B

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Nov 29, 2016

Great course. Material well presented and

创建者 Abhishek G

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Jun 22, 2016

The quizzes can be a bit more challenging

创建者 VITTE

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Jul 18, 2018

Very clear and useful course, excellent.

创建者 Hansel G

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Nov 1, 2017

Great course !!! I totally recommend it.

创建者 Aditi

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Oct 20, 2016

Wonderful experience. Prof is very good.

创建者 Madhusudhan r D

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Jun 27, 2020

Ex ordinary subject with nice concepts.

创建者 Israel C

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May 30, 2017

One of the best courses i've ever tried

创建者 Ma J

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Feb 28, 2022

I like the instructor and his style :D

创建者 Garvish

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Jun 13, 2017

Great Information and organised course

创建者 Lei Q

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Mar 15, 2016

Excellent theory and practice(coding)!

创建者 David P

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Jun 27, 2020

A great course and a great teacher!!!

创建者 Mao M

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May 6, 2019

lots of work. very good for beginners

创建者 Dhruvil S

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Jan 10, 2018

Nice Course Clears a lot of concepts.

创建者 Xue

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Dec 14, 2018

Very good lessons on classification.

创建者 Aayush A

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Jul 16, 2018

very good course for classification.

创建者 Colin B

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Apr 9, 2017

Really interesting course, as usual.

创建者 Jialie Y

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Feb 8, 2019

It is really useful and up to date.

创建者 Sean L

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Aug 31, 2016

wonderful course for beginner of ML