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

4.7
3,474 個評分
577 條評論

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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351 - Machine Learning: Classification 的 375 個評論(共 546 個)

創建者 Hanna L

2019年8月12日

Great class!

創建者 Illia K

2018年10月25日

Very useful!

創建者 Shuang D

2018年6月28日

nice course!

創建者 Krishna C

2017年9月16日

Great Course

創建者 Krzysztof S

2017年6月6日

great course

創建者 Thuong D H

2016年9月22日

Good course!

創建者 Siddharth V B

2020年11月29日

nice course

創建者 SUBBA R D

2020年6月16日

Nice course

創建者 ADITYA P S

2020年4月20日

nice course

創建者 Mr. J

2020年3月22日

spectacular

創建者 Suneel M

2018年5月9日

Excellent c

創建者 Do A T

2017年11月15日

very useful

創建者 李今晖

2016年9月1日

Good course

創建者 Deleted A

2020年5月3日

its useful

創建者 Jan L

2017年8月2日

Just great

創建者 童哲明

2016年7月26日

very goog!

創建者 Jair d M F

2016年4月21日

Very Good!

創建者 Neha K

2020年9月19日

EXCELLENT

創建者 PAWAN S

2020年9月17日

EXCELLENT

創建者 Nidal M G

2018年12月4日

very good

創建者 王曾

2017年11月27日

very good

創建者 Muhammad H S

2016年11月2日

Excellent

創建者 Joshua C

2017年5月3日

Awesome!

創建者 Roberto E

2017年3月1日

awesome!

創建者 Isura N

2017年12月28日

Hoooray