Chevron Left
返回到 Machine Learning: Classification

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

4.7
星
3,739 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.

MP

Aug 22, 2017

The course starts slow, but it gets more interesting from week 2. The assignments are more challenging than in Regression, but I have really enjoyed it. I highly recommend it!

筛选依据：

501 - Machine Learning: Classification 的 525 个评论（共 589 个）

创建者 Charan S

•

Jul 30, 2017

Very nice course, detailed explanations and visualizations.

创建者 Sahil M

•

Jul 10, 2018

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

创建者 Jiancheng Y

•

Mar 20, 2016

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

创建者 Hanqiao L

•

Aug 9, 2016

Need more content for SVM and Random Forest

创建者 Alejandro T

•

Sep 9, 2017

It's a really good course, really liked it

创建者 Mohit G

•

Feb 2, 2019

Good, insightful but repetitive coding.

创建者 Sah-moo K

•

Apr 3, 2016

Decision trees and boosting were great.

创建者 Chitrank G

•

May 10, 2020

The course is excellent for beginners.

创建者 Deleted A

•

Aug 26, 2019

A good course to teach the key points.

创建者 Hexuan Z

•

Oct 5, 2016

could be more challengable homework!!

创建者 Vladislav V

•

May 13, 2016

It feels like it lacks certain depth.

创建者 S G

•

May 22, 2020

Course material can be much better

创建者 Farmer

•

Aug 12, 2018

Exercises are way too easy.

创建者 Aadesh N

•

Jun 13, 2016

Great course materials

创建者 Xiaojie Z

•

Jan 31, 2017

Can be more detailed.

创建者 Ragunandan R M

•

Sep 16, 2018

Good overall course.

创建者 2K18/SE/035 A K

•

Nov 11, 2020

content is complete

创建者 Lim W A

•

Nov 21, 2016

Learnt new things.

创建者 Mehul P

•

Aug 17, 2017

Nice explanation.

创建者 gaozhipeng

•

Jun 30, 2016

good introduction

创建者 Alberto B

•

Mar 17, 2018

Very good course

创建者 Antonio P L

•

Apr 30, 2016

Fantastic Course

创建者 Anand B

•

Aug 6, 2017

Great course!

创建者 PRASAD N

•

Dec 2, 2020

good course.

创建者 ayshwarya s

•

Feb 5, 2019

best course