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返回到 Machine Learning: Classification

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

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3,610 個評分
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597 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)....

熱門審閱

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!

篩選依據：

251 - Machine Learning: Classification 的 275 個評論（共 566 個）

創建者 Abhishek G

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2016年6月22日

The quizzes can be a bit more challenging

創建者 VITTE

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2018年7月18日

Very clear and useful course, excellent.

創建者 Hansel G M

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2017年11月1日

Great course !!! I totally recommend it.

創建者 Aditi R

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2016年10月20日

Wonderful experience. Prof is very good.

創建者 Madhusudhan r D

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2020年6月27日

Ex ordinary subject with nice concepts.

創建者 Israel C

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2017年5月30日

One of the best courses i've ever tried

創建者 Garvish

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2017年6月14日

Great Information and organised course

創建者 Lei Q

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2016年3月16日

Excellent theory and practice(coding)!

創建者 David P

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2020年6月27日

A great course and a great teacher!!!

創建者 MAO M

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2019年5月6日

lots of work. very good for beginners

創建者 Dhruvil S

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2018年1月10日

Nice Course Clears a lot of concepts.

創建者 Xue

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2018年12月14日

Very good lessons on classification.

創建者 Aayush A

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2018年7月16日

very good course for classification.

創建者 Colin B

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2017年4月9日

Really interesting course, as usual.

創建者 Jialie ( Y

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2019年2月8日

It is really useful and up to date.

創建者 Sean L

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2016年8月31日

wonderful course for beginner of ML

創建者 Cosmos D I

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2020年3月29日

This course is very informational!

創建者 Alessandro B

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2017年10月31日

nice, clear engaging ...and useful

創建者 易灿

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2016年11月28日

课程很生动，讲的很详细，真心谢谢导师！希望能在算法后面多提供点资料！

創建者 Henry H

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2016年11月17日

Very clear and easy to understand.

創建者 Albert V d M

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2016年3月8日

Very instructive, you learn a lot.

創建者 Angel S

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2016年3月8日

Awesome. Waiting for the next one.

創建者 Jing

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2017年8月14日

Better than the regression course

創建者 Rishabh J

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2016年12月19日

Amazing course, Amazing teaching.

創建者 CHERUKURI S V N K

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2020年5月29日

IT WAS EXCELLENT AND ENJOYED IT.

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