Chevron Left
Back to Machine Learning: Classification

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

3,470 ratings
577 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

Jun 14, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

Oct 15, 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!

Filter by:

51 - 75 of 545 Reviews for Machine Learning: Classification

By Prashant K

Nov 16, 2020

Amazingly structured course. When I started the course, I did not realise that there would be so much content like Boosting and Stochastic Gradient Descent. It is truly a fantastic course with hands on learning. Cheers to Prof Carlos and team

By ramesh

Mar 31, 2016

I come to know how can i applym machine learning conceps i real world scenarios . The instructors are so nice and always explaining in simple methods. Nice teaching abilities.. Glad to guided under this kind of instructors. Nice experience.

By Marios A

Mar 8, 2016

The course is really well structured and gives a solid understanding in the latest approaches in Machine Learning. However I would also like to see in this course more sophisticated math, because it matters and I think there are important.

By Bert B

Oct 20, 2016

Very well done course.

Would be nice to have many more very short examples during the lectures that match the formulas. This would help me understand the formulas much better since I do not have a calculus or linear algebra background.

By akshay b

May 23, 2017

Excellent and intuitive introduction to classification.Certainly a lighthouse in a rather overwhelming and chaotic learning scenario of machine learning we have now a days(Highly recommended for both mathematics and programming student)

By leonardo d

Dec 2, 2018

This course covered very interesting aspects of real-world applications for machine learning. From my point of view, the theory was very clear an valuable, until that point that the programming assignments closed the cycle beautifully.

By Jafed E G

Jul 6, 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

By Manuel G

Jan 1, 2019

Really awesome course. Nice balance between practical uses, theory, and implementation projects. It's good they kept the "optional" videos for the more detailed discussion instead of just removing that material. Totally recommend it.

By Theodore G

Oct 21, 2016

An interesting series of Lectures in the important topic of Classification. The business case approach followed by the instructors provides great help to apply the required theoretical knowledge and further elaborate these methods.

By B M K

Oct 16, 2016

Challenging and Exciting Course. Lots of ML concepts (Decision Trees, AdaBoost, Ensembles, Stochastic gradient, loglikelyhood etc. ) are introduced and i believe this course is of extreme importance in laying the fundamentals of ML.

By Nitish V

Jul 6, 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 Ahmed N A

May 4, 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.

By David E

Aug 21, 2016

very useful course : covers a range of very practical and useful topics I had heard about but didn't fully understand until taking this course. Some highlights stochastic gradient, boosting, and precision-recall trade offs.


May 12, 2020

After watching Emily's Fox Regression wondered how much better could this professor be...But Mr Guestrin is truly great. Fantastic job, well explained difficult concepts. Probably the best classification course out there

By Shingala J J

Jun 13, 2020

very good course

very good and intelligent instructor

overall very good content and passion course i had learned a lot

very good and awesome efforts by instructor carlose guestin well done

very happy with this course :)

By Iñaki D R

Aug 3, 2020

Excelent course !! The perfect choice if you want to discover for the first time basic classification algorithms, get some practice and knowledge with them, and at the end dive into more complex classification analisys

By Wenxin X

Mar 26, 2016

This specialization overall is pretty good. Personally I feel like Classification talks more about concepts and important ideas and requires less on coding comparing to Regression. Learned a lot! Love Carlos and Emily!

By Punam P

Apr 16, 2020

Very nice course for beginners to enhance the knowledge.. Thanks to Resp. Sir for your clear view for each concept which helps me lot. Thanks to Team & university of Washington.. Really enjoy the study in each module.

By Vishwajeet U O

Oct 1, 2020

Carlos is just an amazing teacher. I love his style of teaching, and his case-study approach is simply fantastic. I also liked the little jokes he made to make the harder parts of the course a bit more bearable. :)

By Maria C

Mar 7, 2016

One of the best online machine learning courses I have taken. Excellent explanation of many techniques on Classification. A great combination of theory and hands-on examples. Thank you, Professors Fox & Guestrin.

By Marcio R

Jun 14, 2016

Curso excelente, desde o material, as atividades práticas e aulas. O fórum de discussões é repleto de pessoas interessadas em ajudar. Essa é a especialização a longa distância definitiva de Machine Learning.

By Sam 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.


Jun 15, 2020

A very deep and comprehensive course for learning some of the core fundamentals of Machine Learning. Can get a bit frustrating at times because of numerous assignments :P but a fun thing overall :)

By M L

Mar 14, 2016

Great course!

Personally I could use a little more on the math behind the algorithms (e.g. Adaboost, why does it work?).

Also, would be great to add SVM in next iterations of this class.


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