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学生对 华盛顿大学 提供的 Machine Learning: Classification 的评价和反馈

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3,682 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

SM

Jun 14, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

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!

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1 - Machine Learning: Classification 的 25 个评论(共 578 个)

创建者 Alex H

Feb 7, 2018

创建者 Lewis C L

Jun 13, 2019

创建者 Saqib N S

Oct 16, 2016

创建者 Ian F

Jul 17, 2017

创建者 RAJKUMAR R V

Oct 2, 2019

创建者 Christian J

Jan 25, 2017

创建者 Jason M C

Mar 29, 2016

创建者 Feng G

Jul 12, 2018

创建者 Saransh A

Oct 31, 2016

创建者 Sauvage F

Mar 29, 2016

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Aug 4, 2018

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Dec 21, 2016

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May 18, 2016

创建者 Ridhwanul H

Oct 16, 2017

创建者 Gerard A

May 18, 2020

创建者 Apurva A

Jun 14, 2016

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Jun 25, 2017

创建者 Benoit P

Dec 29, 2016

创建者 Liang-Yao W

Aug 11, 2017

创建者 Paul C

Aug 13, 2016

创建者 Sean S

Mar 9, 2018

创建者 Ferenc F P

Jan 18, 2018

创建者 Samuel d Z

Jul 10, 2017

创建者 Adrian L

Sep 2, 2020

创建者 Yifei L

Mar 27, 2016