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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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3,594 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!

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326 - Machine Learning: Classification 的 350 個評論（共 566 個）

創建者 Kurt K

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2016年4月16日

Excellent course !

創建者 DEEPAK P

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2020年6月6日

ULTIMATE TEACHING

創建者 Aparna g

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2020年1月30日

very Good Concept

創建者 Germanno T

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2019年12月4日

Excellent Course!

創建者 Miguel Á B P

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2019年5月21日

Excellent course!

創建者 parv j

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2019年3月3日

Brilliant course!

創建者 Mayank C

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2018年4月12日

Loved this course

創建者 Matt Y

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2018年3月10日

Simply excellent!

創建者 Jonathan H

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2017年6月16日

Excellent course!

創建者 Le D L

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2017年5月2日

Lots of knowledge

創建者 Prabal T

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2016年10月5日

Excellent course!

創建者 André F d A F C

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2016年7月25日

Excellent course.

創建者 V S

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2016年4月28日

Best course ever!

創建者 Do H L

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2016年3月10日

Awesooommmmeeeeee

創建者 VijayaLakshmi A

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2021年8月10日

Good explanation

創建者 Sukhvir S

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2020年7月10日

Great Experience

創建者 Phan T B

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2016年4月17日

Very good course

創建者 Michelle B

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2021年7月15日

Well explained.

創建者 PUNEET K G

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2020年8月1日

best course....

創建者 Jesús U S

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2020年6月26日

Awesome Course!

創建者 Jerome Z

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2018年7月4日

Very good class

創建者 Paulo R M B

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2017年1月30日

Well explaned !

創建者 Pandu R

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2016年4月20日

Worth the wait.

創建者 Roberto C

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2020年5月18日

Simply amazing

創建者 HOUESSOU R T

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2020年5月4日

Very well done

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