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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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3,739 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：

351 - Machine Learning: Classification 的 375 个评论（共 589 个）

创建者 HARSH R

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Nov 4, 2024

excellent course

创建者 VijayaLakshmi A

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Aug 10, 2021

Good explanation

创建者 Sukhvir S

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Jul 10, 2020

Great Experience

创建者 Phan T B

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Apr 17, 2016

Very good course

创建者 Mich

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Jul 15, 2021

Well explained.

创建者 PUNEET K G

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Aug 1, 2020

best course....

创建者 Jesús U S

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Jun 26, 2020

Awesome Course!

创建者 Jerome Z

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Jul 4, 2018

Very good class

创建者 Paulo R M B

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Jan 30, 2017

Well explaned !

创建者 Pandu R

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Apr 20, 2016

Worth the wait.

创建者 Roberto C

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May 18, 2020

Simply amazing

创建者 HOUESSOU R T

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May 4, 2020

Very well done

创建者 Manan M

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Apr 20, 2020

Amazing course

创建者 avishek k

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Jul 19, 2020

great content

创建者 Gaurav G

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Dec 26, 2018

Good Course!!

创建者 sankar b

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Oct 30, 2018

Good learning

创建者 Yang X

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Oct 29, 2017

Very helpful!

创建者 Omar B

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Feb 9, 2017

Great course.

创建者 Zizhen W

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Nov 3, 2016

Pretty Solid!

创建者 Manuel S

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Sep 11, 2016

Great course!

创建者 Salim T T

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Apr 13, 2021

Good course!

创建者 Francisco R M

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Feb 21, 2021

Great course

创建者 Sumit K J

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Jan 24, 2021

Great Course

创建者 Aaqib M

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Sep 20, 2020

Great course