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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

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.

DF

Aug 6, 2016

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

筛选依据：

476 - Machine Learning: Classification 的 500 个评论（共 589 个）

创建者 Yingnan X

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Apr 14, 2016

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

创建者 Oleg R

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Oct 9, 2016

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

创建者 Thrivikrama

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Oct 12, 2016

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

创建者 Tomasz J

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Apr 3, 2016

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

创建者 Baubak G

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Jun 10, 2018

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

创建者 Simon C

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May 1, 2020

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

创建者 Scott A

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Jul 19, 2021

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

创建者 Srinivas C

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Dec 2, 2018

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

创建者 HIMBERT F

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Aug 19, 2023

Good level

Assignements based on SFrame. Can be adaptated to pandas but that's not so obvious.

创建者 Sapna A

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Feb 2, 2021

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

创建者 ZhangBoyu

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Jul 20, 2018

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

创建者 shashank a

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Jun 9, 2020

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

创建者 Rattaphon H

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Aug 13, 2016

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

创建者 Bruno G E

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Apr 17, 2016

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

创建者 Jacob M L

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Jun 24, 2016

Very approachable material, given the diversity of classification algorithms.

创建者 hiram y s

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Apr 26, 2020

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

创建者 Luiz C

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Jun 7, 2018

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

创建者 Zebin W

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Aug 23, 2016

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

创建者 Luis d l O

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Jun 22, 2016

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

创建者 Sander v d O

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May 9, 2016

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

创建者 Franklin W

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May 4, 2017

Great beginner/advanced course for Machine Learning Classification!

创建者 Pascal U E

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Mar 7, 2016

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

创建者 Harshit P

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Oct 3, 2022

This is the perfect course but could be better if we use Sklearn

创建者 Michael B

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Sep 4, 2016

Good survey of the material, but assignments are superficial.

创建者 vardan l

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Jan 26, 2018

Some instructions in programming assignments are not clear.