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

2,925 個評分
485 個審閱


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)....



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!


Jan 25, 2017

Very impressive course, I would recommend taking course 1 and 2 in this specialization first since they skip over some things in this course that they have explained thoroughly in those courses


226 - Machine Learning: Classification 的 250 個評論(共 453 個)


Aug 01, 2016

The course has be described in a very precise manner. The instructor takes time to clearly explain the concepts and the importance of the same.

創建者 Isaac B

Nov 20, 2016

Excellent course. Practical understanding of classification

創建者 Wenxin X

Mar 26, 2016

This specialization overall is pretty good. Personally I feel like Classification talks more about concepts and important ideas and requires less on coding comparing to Regression. Learned a lot! Love Carlos and Emily!

創建者 Maria C

Mar 08, 2016

One of the best online machine learning courses I have taken. Excellent explanation of many techniques on Classification. A great combination of theory and hands-on examples. Thank you, Professors Fox & Guestrin.

創建者 Niyas M

Oct 29, 2016

Amazing course! Packed with insights, reasoning and Carlos's humor and wit. Highly recommended for novices (along with the Machine Learning Foundations course).

創建者 Krzysztof S

Jun 06, 2017

great course

創建者 Dhritiman S

Feb 09, 2017

These courses have been a perfect mix of theory and practice. Looking forward to the final two courses in the specialization getting released at some point in the future :)

創建者 Prabal T

Oct 05, 2016

Excellent course!

創建者 Fakrudeen A A

Sep 15, 2018

Excellent course - teaches linear, logistic regression and decision trees. It also teaches the most important concept of precision-recall. Overall highly recommended.

創建者 Chandan D

Aug 25, 2018

I really enjoyed learning this course on Machine Learning Classification!

創建者 Maxwell N M

Oct 07, 2018

Great Course!

Teachers are genius and awesome


創建者 Arun K P

Oct 17, 2018


創建者 Courage S

Oct 22, 2018

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

創建者 Illia K

Oct 25, 2018

Very useful!

創建者 Thomas K

Oct 29, 2018

In my opinion, so far the best part in the specialization series. The only thing, that was strange for me is that the effort required for programming varied a lot. So from week to week, it was difficult to predict how much time and effort would be needed to finish the assignments in time.


Oct 30, 2018

Good learning


Jul 18, 2018

Very clear and useful course, excellent.


Aug 23, 2018

The course is good. The materials are amazing!

創建者 Naimisha S

Jul 30, 2018

Availability of the Ipython notebook makes it easy to solve the Quizzes which has step by step explaination

創建者 MAO M

May 07, 2019

lots of work. very good for beginners

創建者 Miguel Á B P

May 21, 2019

Excellent course!

創建者 Gaurav C

May 22, 2019

Would have loved even more had Carlos explained his students gradient boosting as well. I liked the way of his taught in lectures.

創建者 Kevin

Aug 07, 2019

Great course for beginner to intermediate data science enthusiast! This course teaches you how to implement logistic regression, decision tree, AdaBoost algorithm, and stochastic approach from scratch! There's also some assignment to learn how to implement those algorithms in our preferred library. Would be great if Carlos & Emily can bring another advanced machine learning course!

創建者 Karthik M

Jun 01, 2019

Excellent course and the instructors cover all the important topics

創建者 akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable