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

2,883 ratings
479 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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276 - 300 of 447 Reviews for Machine Learning: Classification

By Fernando B

Feb 21, 2017

Best Course on ML yet on the Web

By Joshua C

May 03, 2017


By Manuel S

Sep 11, 2016

Great course!

By Binil K

Jul 30, 2016

Nice Course, very much helpful and reccomended

By Dhritiman S

Feb 09, 2017

These courses have been a perfect mix of theory and practice. Looking forward to the final two courses in the specialization getting released at some point in the future :)

By Renato R S

Aug 27, 2016

All the basics - and much of the advanced stuff - is presented, in a coherent and inspired way. Thanks for crafting such a course.

By Mayank C

Apr 12, 2018

Loved this course

By Alexander S

Aug 07, 2016

one of the best courses.

By Usman

Nov 13, 2016

I think support vector machines is an important topic which is missing. Anyway, the programming assignments were terrific. I really enjoyed this course!

By Renato V

Jul 13, 2016

A very good course, with effective intuitive explanations of what the algorithms are supposed to achieve and how. The exercises in Python help understand the topic and fix it in memory.

By Ashish

Oct 26, 2016

I appreciate the way Emily and Carlos explain the concepts. Its very intuitive for beginners and optional sections give further details. The datasets used in programming assignments are taken from real world examples.

Overall an excellent course and really looking forward to completing the series.

Kudos to Carlos, Emily and the team.

By Syed A u R

Aug 11, 2016

exceptional course. Carlos did an excellenet job

By Prabal T

Oct 05, 2016

Excellent course!

By V S

Apr 28, 2016

Best course ever!

By Anwarvic

Dec 05, 2016

This course is awesome, specially the assignments. In this course, I've implemented most of the famous ML algorithms that our world is now using.

I can't describe how happy I am. Before this course, I looked at machine learning as a difficult field which can't be understood no matter what. Today, I'm capable of doing some great effort.

Thank You so much :)

By Shashidhar Y

Apr 02, 2019



May 03, 2019

This course will provide you clear and detailed explanation of all the topics of Classification.


May 07, 2019

lots of work. very good for beginners

By Shazia B

Mar 25, 2019

one of the best experience about this course i gained I learned a lot about machine learning classification further machine learning regression thanks a lot Coursera :)

By akashkr1498

May 19, 2019

good course but make quize and assignment quize more understandable

By Dohyoung C

Jun 04, 2019

Great ...

I learned quite a lot about classification

By Md s

Jun 09, 2019

awesome course , have learned lot of stuff

By sudheer n

Jun 12, 2019

The way Carlos Guestrin explains things is exquisite. if basics is what is very important to you, and can learn code implementation and libraries from other sources, this is the go to course

By lokeshkunuku

Jun 12, 2019

its been 3 weeks I started this course it was so nice and awesome. the lectures explaination and the ppt all were well crafted and easy to pick and understand.

By Jafed E

Jul 06, 2019

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand