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學生對 华盛顿大学 提供的 Machine Learning: Classification 的評價和反饋

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3,671 個評分

課程概述

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)....

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SM

2020年6月14日

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

2016年10月15日

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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251 - Machine Learning: Classification 的 275 個評論(共 576 個)

創建者 Md s

2019年6月9日

創建者 Fabiano B

2017年7月21日

創建者 PAVITHRA B

2020年9月29日

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2017年5月29日

創建者 Ashley B

2016年11月29日

創建者 Abhishek G

2016年6月22日

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2018年7月18日

創建者 Hansel G M

2017年11月1日

創建者 Aditi R

2016年10月20日

創建者 Madhusudhan r D

2020年6月27日

創建者 Israel C

2017年5月30日

創建者 Ma J

2022年2月28日

創建者 Garvish

2017年6月14日

創建者 Lei Q

2016年3月16日

創建者 David P

2020年6月27日

創建者 MAO M

2019年5月6日

創建者 Dhruvil S

2018年1月10日

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2018年12月14日

創建者 Aayush A

2018年7月16日

創建者 Colin B

2017年4月9日

創建者 Jialie ( Y

2019年2月8日

創建者 Sean L

2016年8月31日

創建者 Cosmos D I

2020年3月29日

創建者 Alessandro B

2017年10月31日

創建者 易灿

2016年11月28日