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

2,906 ratings
482 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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176 - 200 of 450 Reviews for Machine Learning: Classification

By Roger S

Sep 04, 2016

This course is COOL

By Mike M

Jul 16, 2016

Learned a lot, great course!

By Daopeng S

Apr 12, 2016

A very good introduce machine learning course, it's clear and easy to follow.

By Kuntal G

Nov 03, 2016

Great course with detail explanation ,hands-on lab along with some advance topic. Really a great course for anyone interested in the field of real world machine learning

By Lars N

Oct 04, 2016

Best course taken so far!

By vacous

Aug 03, 2017

very nice material covering the basic of classification.

By Matt Y

Mar 10, 2018

Simply excellent!

By Yuexiu C

Jan 20, 2017

The instructor is awesome. He explained the boring statistical method in a very interesting way!

By Jinho L

Jul 20, 2016

Very pragmatic and interesting

By Chintamani K

Oct 10, 2017

In detail course for understanding the various concepts of classification. Instead of relying on the libraries, the course focuses on teaching the algorithm implementation using coding language of user's choice. This helps in understanding the algorithms better.

By Mansoor A B

May 02, 2016

I think this is an excellent course to give an idea about the machine learning concept of classification. I felt the lectures were to the point, straight forward and more importantly dealt with practical issues and solutions. The assignments are pretty cool, though large amount of code is written at a few points - I still found them pretty engaging.

By Ornella G

Oct 01, 2016

I really enjoyed the topics presented and the fluid way to present them. It's a very well done summary of the classification models.

By Sudip C

May 03, 2016

Very detailed, Liked optional sections also. Loved it.

By Christian R

Sep 11, 2017

The visualizations provide deeper understanding in the algorithms.

By Omar B

Feb 09, 2017

Great course.

By Edward F

Jun 25, 2017

I took the 4 (formerly 6) courses that comprised this certification, so I'm going to provide the same review for all of them.

This course and the specialization are fantastic. The subject matter is very interesting, at least to me, and the professors are excellent, conveying what could be considered advanced material in a very down-to-Earth way. The tools they provide to examine the material are useful and they stretch you out just far enough.

My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning. I hope they get to do that some day.

By clara c

Jun 11, 2016

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

By Michael P

Dec 06, 2016

Awesome, not awful;)

By Sean L

Aug 31, 2016

wonderful course for beginner of ML

By Kim K L

Aug 13, 2016

Another classic and fantastic. Love this Course and learn so much. Highly recommended!

By Sarah W

Sep 24, 2017

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

By Jooho S

Jul 01, 2016

It's very practical.

By Weituo H

Mar 14, 2016

Useful and interesting~

By Rashi K

Mar 17, 2016

Assignments were more challenging than previous course. Loved solving them. Enjoyed the optional videos.

By Phan T B

Apr 17, 2016

Very good course