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Learner Reviews & Feedback for Machine Learning: Classification by University of Washington

3,688 ratings

About the Course

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)....

Top reviews


Jun 14, 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 15, 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!

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301 - 325 of 579 Reviews for Machine Learning: Classification

By Lixin L

May 7, 2017

really good course. thanks


May 9, 2020


By Satish K D

Feb 3, 2019

it was easy to understand

By FanPingjie

Dec 9, 2018

useful and helpful course

By Lars N

Oct 4, 2016

Best course taken so far!

By Venkata D

Apr 14, 2016

Great course and learning

By Brian N

May 20, 2018

Nice to learn this topic

By Mark h

Jul 27, 2017

Very Helpful Material!!!

By Shiva R

Apr 16, 2017

Exceptional and Intutive

By Shanchuan L

Dec 7, 2016

This is a perfect course

By Changik C

Oct 25, 2016

Learned a lot recommend!

By Alexander S

Aug 7, 2016

one of the best courses.

By Yacine M T

Jul 31, 2019

Very helpful. Thank you

By Fakhre A

Feb 17, 2017

Outstanding Course.....

By Weituo H

Mar 14, 2016

Useful and interesting~

By Gaurav K

Sep 19, 2020

Very good course to do


May 24, 2020

Excellent Course.....

By Kevin Y

Jun 26, 2017

Very good instructors

By Sami A

May 20, 2016

The best in the field

By stephon_lu

Dec 23, 2017

very good! thank you

By Michael P

Dec 6, 2016

Awesome, not awful;)

By 쥬

Jun 30, 2016

It's very practical.


Oct 13, 2019

Excellent tutorials

By Muhammad Z H

Aug 30, 2019

I have learned alot

By Luis E T N

Jul 4, 2017

Excelent! Congrats!