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

2,923 ratings
485 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

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226 - 250 of 453 Reviews for Machine Learning: Classification

By Frank Z

Jul 04, 2018

Very good class

By A S P

Nov 14, 2016

Informative with useful assignments and optional lectures that provide a deeper mathematical understanding. Great for newbies as well as more seasoned computer scientists looking to expand into new material.

By Konstantinos P

Mar 28, 2017

The context and the structure of the course is absolutely perfect. Also, Carlos is the perfect professor!

By japneet s c

Feb 06, 2018

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

By Jing

Aug 14, 2017

Better than the regression course

By Le L

May 02, 2017

Lots of knowledge

By Samuel d Z

Jul 10, 2017

AWESOME!!! Very well structured. Concepts are explained in small and short videos which focus on one thing at the time. Unnecessary clutter is removed and deep dives can now be done with this solid foundation. Also the Python programming part teaches so much and again, only asked to program the essentials and non essentials or "special tricks" are done, so you can see and learn from them without having to search on the web. THANKS.

By venkatpullela

Nov 17, 2016

Course is really good. Assignments are taking too much time if you want to do the course rally fast, with questionable learning value.

By Tewende J E K

Jul 24, 2016

intuitive, clear and practical. The best explanation I found so far !

By Freeze F

Jun 07, 2016

This lecture gave a great start for me into ML . :) :)

By Xuan Q

Feb 14, 2017

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

By Srinivas J

Nov 12, 2016

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

By xiaofeng y

Feb 06, 2017


By Ashley B

Nov 30, 2016

Great course. Material well presented and

By Josef H

Nov 27, 2016

I like the detailed comparison between choosing different parameters for creating the classification model. I learn a lot of tricks for creating plots.

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 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 Trinh Q N

Jan 29, 2018

Give me a good understanding of Classification

By Brian N

May 20, 2018

Nice to learn this topic

By kumar A

Jun 05, 2018

great course for beginners

By Do A T

Nov 15, 2017

very useful

By Suneel M

May 09, 2018

Excellent c

By Phil B

Feb 13, 2018

Excellent overview of the most commonly used Classification techniques, providing the wireframe for us to write our own algorithms from scratch. Really enjoyed this one.


Nov 18, 2017

Very interesting. It's easy to understand. Thanks