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

3,611 ratings
597 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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451 - 475 of 566 Reviews for Machine Learning: Classification

By Fahad S

Nov 3, 2018

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

By Aaron

Jul 3, 2020

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

By Alexis C

Sep 29, 2016

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

By Kishaan J

Jul 1, 2017

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

By Raisa M

Aug 19, 2017

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

By Ning A

Sep 16, 2016

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

By Yingnan X

Apr 14, 2016

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

By Oleg R

Oct 9, 2016

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

By Thrivikrama

Oct 12, 2016

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

By Tomasz J

Apr 4, 2016

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

By 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.

By Simon C

May 1, 2020

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

By Scott A

Jul 19, 2021

Class was inconsistent, it started very detailed and became over-simplified in the later weeks.

By Srinivas C

Dec 2, 2018

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

By Sapna A

Feb 2, 2021

The course was awesome, especially with sentimental classification case explanation... Thanks

By ZhangBoyu

Jul 20, 2018

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

By shashank a

Jun 9, 2020

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

By Rattaphon H

Aug 13, 2016

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

By Bruno G E

Apr 17, 2016

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

By Jacob M L

Jun 24, 2016

Very approachable material, given the diversity of classification algorithms.

By hiram y s

Apr 26, 2020

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

By Luiz C

Jun 7, 2018

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

By Zebin W

Aug 24, 2016

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

By Luis d l O

Jun 22, 2016

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

By Sander v d O

May 9, 2016

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