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

2,875 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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151 - 175 of 447 Reviews for Machine Learning: Classification


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

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 Siddharth S

Jan 09, 2018

Excellent course and all the concepts have been explained very simply and with an element of fun.

Many thanks to Emily and Carlos...

By Pardha S M

Jun 02, 2017

All the quiz and programming assignments prepared such away that student can easily get into the workflow, concentrating more on concepts without taking much overhead of programming yet need to think rigorously while writing that small portion of "YOUR CODE" parts on couple of occasions

By Ayush K G

Nov 01, 2017

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

By Fan D

Feb 02, 2017

This course is alright. For some reason I liked the regression course more as this one was a little to simple in terms of the practical.

By Fakhre A

Feb 17, 2017

Outstanding Course.....

By Prajna P

Dec 18, 2017

I enjoyed this course a lot. The case study approach and the optional videos are full of intuitions and I love the way instructors put across the concepts very clearly ... Thank you so much

By Patrick P

Nov 28, 2016

Very good and and informative to start with this subject.

By Koen O

Apr 14, 2017

Excellent course for learning the basics on classification

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 Mark W

May 06, 2017

Fantastic Lecturers and very interesting and informative course

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 Ferenc F P

Jan 18, 2018

This is a very good course in classification. Starts with logistic regression (w. and wo. regularization) and then makes a very good introduction to decision trees and boosting. Also has a very good explanation about stochastic gradient descent. The only drawback is that for some Quizes the result is different with scikit-learn than with Graphlab while the Quiz is prepared for Graphlab results. Thus, with scikit-learn one may fail some of them.

By Fabiano B

Jul 21, 2017

It is a very good course. Congratulations!

By Mark h

Jul 27, 2017

Very Helpful Material!!!

By alireza r

May 29, 2017

It is really engaging and well explained.

By Navinkumar

Feb 23, 2017


By Dauren B

Jan 27, 2018

I loved this course! It is designed in a way that both beginner and more advanced student can grasp knowledge. New things for me like boosting (ensemble models), decision trees, stochastic gradient descent, online learning (which is not used much by big systems, instead they tend to do something different for incoming new data) and much more are introduced and explained in this course. Recommend 10/10.

By Nitish V

Jul 06, 2017

The course is well designed for both beginners and experts . The concepts are well explained and the assignments are really challenging. Best thing is , it talks more from practical aspects . The optional sections are really good.

By Ankit S

Jun 08, 2016

Really nice course!

By Pranas B

Jul 01, 2016

Good practice and bit of theory.