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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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3,612 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：

176 - Machine Learning: Classification 的 200 個評論（共 566 個）

創建者 Michael O T

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2019年11月29日

A great professor and a lot of knowledge about machine learning classification

創建者 Suresh K P

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2017年12月19日

This course much helpful and understandable easily compared previous sessions.

創建者 Daopeng S

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2016年4月12日

A very good introduce machine learning course, it's clear and easy to follow.

創建者 Daniel Z

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2016年3月8日

This is a hand-on very exciting course, strongly recommended for all audience

創建者 Xavi R

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2021年1月19日

This is a great course! The professors are great and the material is clear!

創建者 Vladimir V

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2017年6月14日

Awesome course! Highly recommend for anyone interested in machine learning.

創建者 James M

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2016年7月20日

Top notch. Great course design. Best value for money in Machine Learning!

創建者 Javier A

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2018年11月25日

Quite Interesting. Entertaining and the lectures are quite easy to follow.

創建者 Kazi N H

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2016年6月23日

One of the awesome course on classification. Just so perfect for learning.

創建者 Chandan D

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2018年8月25日

I really enjoyed learning this course on Machine Learning Classification!

創建者 Zuozhi W

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2017年2月8日

Very informative class! The lectures are slow, clear, and easy to follow.

創建者 Pankaj K

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2017年9月25日

Great challenging and deep assignments! Big Thanks to both professors!!

創建者 Zhongkai M

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2019年2月12日

Great course, provided details that not show in others' and textbooks.

創建者 courage s

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2018年10月22日

Excellent Teaching with meticulous details and great humor. BIG Plus.

創建者 Jean-Etienne K

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2016年7月24日

intuitive, clear and practical. The best explanation I found so far !

創建者 akashkr1498

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2019年5月19日

good course but make quize and assignment quize more understandable

創建者 Alexandre N

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2016年12月20日

Excellent course with plenty of intuition and practical experiments.

創建者 eric g

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2016年3月21日

The best part for me in this specialization, Classification is great

創建者 Swapnil A

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2020年9月6日

Really awesome course. Dr. Carlos explains everything from scratch.

創建者 Karthik M

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2019年6月1日

Excellent course and the instructors cover all the important topics

創建者 Srinivas J

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2016年11月12日

truly enjoyed this course and recommended to my colleagues as well.

創建者 Thierry Y

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2017年11月12日

Great material, easy to follow, and nice examples around sushis :)

創建者 Christian R

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2017年9月11日

The visualizations provide deeper understanding in the algorithms.

創建者 Luis M

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2017年1月28日

Lots of practical tips, some applicabe not only to Classification.

創建者 Yoshifumi S

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2016年5月8日

As always in this specialization, tough course but so practical !!

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