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

3,640 ratings
601 reviews

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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251 - 275 of 570 Reviews for Machine Learning: Classification

By Ashley B

Nov 29, 2016

Great course. Material well presented and

By Abhishek G

Jun 22, 2016

The quizzes can be a bit more challenging


Jul 18, 2018

Very clear and useful course, excellent.

By Hansel G M

Nov 1, 2017

Great course !!! I totally recommend it.

By Aditi R

Oct 20, 2016

Wonderful experience. Prof is very good.

By Madhusudhan r D

Jun 27, 2020

Ex ordinary subject with nice concepts.

By Israel C

May 30, 2017

One of the best courses i've ever tried

By Garvish

Jun 14, 2017

Great Information and organised course

By Lei Q

Mar 16, 2016

Excellent theory and practice(coding)!

By David P

Jun 27, 2020

A great course and a great teacher!!!


May 6, 2019

lots of work. very good for beginners

By Dhruvil S

Jan 10, 2018

Nice Course Clears a lot of concepts.

By Xue

Dec 14, 2018

Very good lessons on classification.

By Aayush A

Jul 16, 2018

very good course for classification.

By Colin B

Apr 9, 2017

Really interesting course, as usual.

By Jialie ( Y

Feb 8, 2019

It is really useful and up to date.

By Sean L

Aug 31, 2016

wonderful course for beginner of ML

By Cosmos D I

Mar 29, 2020

This course is very informational!

By Alessandro B

Oct 31, 2017

nice, clear engaging ...and useful

By 易灿

Nov 28, 2016


By Henry H

Nov 17, 2016

Very clear and easy to understand.

By Albert V d M

Mar 8, 2016

Very instructive, you learn a lot.

By Angel S

Mar 8, 2016

Awesome. Waiting for the next one.

By Jing

Aug 14, 2017

Better than the regression course

By Rishabh J

Dec 19, 2016

Amazing course, Amazing teaching.