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

3,205 個評分
534 條評論


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)....



Jun 15, 2020

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 :)


Oct 16, 2016

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!


401 - Machine Learning: Classification 的 425 個評論(共 502 個)

創建者 Aaron

Jul 04, 2020

Nice course for new learner of machine learning, but I do hope this course could have introduction to support vector machine.

創建者 Alexis C

Sep 29, 2016

wanted more sophisticated mathematics and intuition (as opposed to simpler explanations). [regression course had this ...]

創建者 Kishaan J

Jul 01, 2017

Really loved this course! The insights into decision trees and precision-recall couldn't have been any better! Thank you!

創建者 Raisa

Aug 19, 2017

Wanted some stuff on SVM and Dimensionality Reduction. Awaiting for a course on Recommender Systems and Deep Learning

創建者 Ning A

Sep 16, 2016

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

創建者 Yingnan X

Apr 14, 2016

A good course to start learning classifications and getting exposure to algorithms. The instructor is awesome!!

創建者 Oleg R

Oct 09, 2016

I would prefer more complex assignments and more advanced math concepts in the course. Otherwise it is great.

創建者 Thrivikrama

Oct 12, 2016

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

創建者 Tomasz J

Apr 04, 2016

Great course! However I put only 4 starts because I would like to see random forests which are not present.

創建者 Baubak G

Jun 10, 2018

I think the course on boosting could be worked on better. But all in all I really enjoyed this course.

創建者 Simon C

May 01, 2020

It's still a great course. But I think the quality of the regression one is better than this overall.

創建者 Srinivas C

Dec 02, 2018

This course was really good and helped in understanding different techniques in Classification

創建者 ZhangBoyu

Jul 20, 2018

The lecturer speaks in a quite unclear manner, besides, everything is great and detailed.

創建者 Shashank A

Jun 09, 2020

Overall good, But it seems like same type of questions are repeated in assignment quiz

創建者 Rattaphon H

Aug 13, 2016

The questions are hard to understand and ambiguous though their answers are easy.

創建者 Bruno G E

Apr 17, 2016

Lack some of classical classification algorithms like SVM and Neural Netwroks.

創建者 Jacob M L

Jun 24, 2016

Very approachable material, given the diversity of classification algorithms.

創建者 hiram y s

Apr 26, 2020

Very well explained and with careful guidance through the programming steps.

創建者 Luiz C

Jun 07, 2018

Clear, good engaging videos, good quality/complexity balance of exercises

創建者 Zebin W

Aug 24, 2016

It covers many aspects in clustering and the assignments are very helpful

創建者 Luis d l O

Jun 22, 2016

Very easy to follow and didactic. Very good material in the assignments.

創建者 Sander v d O

May 09, 2016

Simply a great course. Good intro to machine learning classifiation.

創建者 Franklin W

May 04, 2017

Great beginner/advanced course for Machine Learning Classification!


Mar 07, 2016

Take you too long to come back, but the content is great. Good job

創建者 Michael B

Sep 04, 2016

Good survey of the material, but assignments are superficial.