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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

SS

Oct 15, 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!

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：

376 - Machine Learning: Classification 的 400 个评论（共 589 个）

创建者 Manikant R

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Jun 19, 2020

Great course

创建者 Dr. R R N

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May 11, 2020

Nice course.

创建者 Hanna L

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Aug 12, 2019

Great class!

创建者 Illia K

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Oct 25, 2018

Very useful!

创建者 Shuang D

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Jun 28, 2018

nice course!

创建者 Krishna C

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Sep 15, 2017

Great Course

创建者 Krzysztof S

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Jun 6, 2017

great course

创建者 Thuong D H

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Sep 22, 2016

Good course!

创建者 Siddharth V B

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Nov 29, 2020

nice course

创建者 SUBBA R D

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Jun 15, 2020

Nice course

创建者 Aditya P S

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Apr 20, 2020

nice course

创建者 Mr. J

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Mar 22, 2020

spectacular

创建者 Suneel M

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May 8, 2018

Excellent c

创建者 Do A T

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Nov 15, 2017

very useful

创建者 Jinhui L

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Sep 1, 2016

Good course

创建者 Ayesha N

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Jul 19, 2021

good stuff

创建者 Deleted A

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May 3, 2020

its useful

创建者 Jan L

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Aug 2, 2017

Just great

创建者 童哲明

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Jul 26, 2016

very goog!

创建者 Jair d M F

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Apr 21, 2016

Very Good!

创建者 Neha K

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Sep 19, 2020

EXCELLENT

创建者 PAWAN S

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Sep 16, 2020

EXCELLENT

创建者 Nidal M G

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Dec 4, 2018

very good

创建者 王曾

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Nov 26, 2017

very good

创建者 Hashim S

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Nov 1, 2016

Excellent