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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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476 - Machine Learning: Classification 的 500 個評論(共 576 個)

創建者 shashank a

2020年6月9日

創建者 Rattaphon H

2016年8月13日

創建者 Bruno G E

2016年4月17日

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2020年4月26日

創建者 Luiz C

2018年6月7日

創建者 Zebin W

2016年8月24日

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2016年6月22日

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2016年5月9日

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2017年5月4日

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2016年3月7日

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2022年10月3日

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2016年9月4日

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2018年7月10日

創建者 Jiancheng

2016年3月20日

創建者 Hanqiao L

2016年8月9日

創建者 Alejandro T

2017年9月9日

創建者 Mohit G

2019年2月2日

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2016年4月3日

創建者 Chitrank G

2020年5月10日

創建者 Gareth W J

2019年8月26日

創建者 Hexuan Z

2016年10月6日

創建者 Vladislav V

2016年5月13日