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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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201 - 225 of 579 Reviews for Machine Learning: Classification

By Thierry Y

Nov 12, 2017

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

By Christian R

Sep 11, 2017

The visualizations provide deeper understanding in the algorithms.

By Luis M

Jan 28, 2017

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

By Yoshifumi S

May 8, 2016

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

By Japneet S C

Feb 5, 2018

Course is very good. Concepts are explained in a very simple way.

By dragonet

Mar 24, 2016

thank you every much, every helpful! ~i will repeat several time~

By Mark W

May 6, 2017

Fantastic Lecturers and very interesting and informative course

By D D

Oct 16, 2016

Nice videos. Learned a lot. Also videos good for future review.

By Eric N

Oct 11, 2020

Excellent online teaching with clear and concise explanations!

By Parab N S

Oct 12, 2019

Excellent course on Classification by University of Washington

By Mohd A

Aug 14, 2016

Learning is fun when you have professors like Carlos Guestrin.

By Ali A

Sep 4, 2017

the course material is great but the assignments are not good

By clara c

Jun 11, 2016

This course was great! I really enjoyed it and learned a lot.

By Yufeng X

Jun 14, 2019

The lecture is super. The exams could be more challenging-:)

By Sarah W

Sep 24, 2017

Great course! Learned so much! So excited to use this stuff!

By Tony T

Nov 19, 2016

funny and enthusiastic lecturer make a dry subject more fun.

By Simbarashe M

Sep 24, 2020

l know a knew way to train the models taught in this course

By Isaac B

Nov 20, 2016

Excellent course. Practical understanding of classification

By Ali A

Mar 21, 2016

So far it is a mazing. I will rate at the end of the course

By Kartik W

Sep 19, 2020

A must do course for all the machine learning enthusiasts.

By Koen O

Apr 14, 2017

Excellent course for learning the basics on classification

By Chao L

Mar 31, 2017

Nicely formatted. And it's quite intuitive and practical.

By Patrick P

Nov 28, 2016

Very good and and informative to start with this subject.

By vacous

Aug 3, 2017

very nice material covering the basic of classification.

By Xuan Q

Feb 13, 2017

Super useful and a bit of challenging! Really enjoy it.