Chevron Left
Back to Machine Learning: Classification

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

2,848 ratings
475 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


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

Filter by:

76 - 100 of 443 Reviews for Machine Learning: Classification

By Nguyen D P

Dec 20, 2017

This course is so good. I can understand the algorithm and know the way how i can apply this for real life. Thanks so much and Washinton university made the wonderful job for everybody. After this course i changed vision, innovation and i think people like me.

By Jonathan H

Jun 16, 2017

Excellent course!

By Suresh K P

Dec 19, 2017

This course much helpful and understandable easily compared previous sessions.

By stephon_lu

Dec 23, 2017

very good! thank you

By Thierry Y

Nov 12, 2017

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

By Sean S

Mar 09, 2018

I am generally very happy with the style, pace, and content of this entire specialization. This course is no exception and exposed me to a lot of new concepts and helped me to improve my python programming skills. I am left wondering if the programming assignments were made easier over time given all of the hints and "checkpoints" for code that was already supplied. I understand this is not a programming course but I probably would have been okay with toiling away at the algorithms for a few more hours without the hints. But that's just me. Great course.

By Ankur P

May 29, 2018

Loved the way our tutor (Carlos) explained the concepts to us. Things are getting clearer with each course in ML :) Many thanks :)

By Brian N

May 20, 2018

Nice to learn this topic

By kumar A

Jun 05, 2018

great course for beginners

By Ayush K G

Nov 01, 2017

Usefull for getting ideas and depth knowledge in Classification. Explained in very simple way.

By Suneel M

May 09, 2018

Excellent c

By Norman O

Feb 19, 2018

I really liked this section on classification. Like with the regression course, complex concepts were explained well with nice examples and assignments. The only issue I had was that some of the coursework can be computing intensive (no surprise there). On the other hand, you really do learn by doing. And, of course, in the real world, computing resources (though plentiful) aren't infinite.

By Rodrigo T

Dec 30, 2017

Excellent course, i really like the general concepts

By Hansel G M

Nov 01, 2017

Great course !!! I totally recommend it.

By Abhijit P

Oct 25, 2017

Excellent course. Loved getting into the details of classification. This was a bit loaded with couple of quizzes as well as assignments in each module. Some questions were tricky and had to go through the videos again to figure out the correct answer. Carlos explained all the concepts very well

By Alessandro B

Oct 31, 2017

nice, clear engaging ...and useful

By Anurag U

Jan 16, 2017

Best source to learn classification techniques

By Hugo L M

May 18, 2018

Very nice feelings from this course. Nice teacher, nice contents and very nice assignements, everything very well structured. As you can see the sentiment coming from my review is a clear +1, so I hope the algorithm looking for good reviews to show to other posible students chooses mine to show up!

By Roberto E

Mar 01, 2017


By Ian F

Jul 18, 2017

Good overview of classification. The python was easier in this section than previous sections (although maybe I'm just better at it by this point.) The topics were still as informative though!

By Srinivas J

Nov 12, 2016

truly enjoyed this course and recommended to my colleagues as well.

By Dmitri T

Apr 25, 2016

Really liked the practical application of this course - very useful in learning classification methods.

By 李真

Mar 06, 2016


By Ahmed N A

May 04, 2018

The best course I could find to get a strong hold of the basics of machine learning. Presented in very easy to follow steps with thorough coverage of all the concepts necessary to understand the big picture of each algorithm.


Mar 26, 2017

effective teaching and practice about decision tree, boosting, and logistic regression. Could have a little more practice on gradient boosted tree/random forest