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

3,600 ratings
597 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!

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426 - 450 of 566 Reviews for Machine Learning: Classification

By Naveendhar

Aug 9, 2019

Last portion was a little difficult to relate to why we started this move for large datasets in the first place. I had to keep going to the fact that I am going to be handling large datasets. Like the use cases. simple and effective. The quizzes were simple and the graph questions were really helpful in gauging my understanding of math behind these models.

By Stefano T

Mar 15, 2016

The contents are very interesting and well explained. Nevertheless, unlike the Regression module, the current one suffers of some technical problem, like slides not well formatted, noisy audio in some video, weekly work load not perfectly calibrated. Despite all this, if you are interested in the subject, you will definitely love this course!!!

By Marku V d S

Dec 23, 2017

I loved the course. Carlos Guestrin is an excellent and engaging professor that really captivate me to work hard to accomplish the assignments.

I just suggest that the assignments should be divided into small pieces to be taken as long the week is accomplished. I felt bad some weeks that had a lot of videos to watch before the first assignment.

By Lorenzo L

Aug 31, 2018

Good, funny and super-clear professors introduce you to the main classification techniques out there (except for neural networks). Great if you are approaching this field and want to know more before deciding if you really want to invest a lot in it. 4 stars because it would have been better with more popular python packages than GraphLab.

By Craig B

Dec 19, 2016

Not as evenly paced as the first two courses. Also some material was covered at a very high level, whilst I found that some explanations did not immediately build on my understanding gained through the foundation course, but rather confused it. Still a worthwhile course nonetheless. I look forward to the rest in the specialisation.

By Nitin K M

Nov 6, 2019

The course is perfect for people who want to gain in-depth knowledge of classification algorithms but exercise descriptions are vague. I found trouble understanding the flow of assignments. Also, Bagging and Gradient Boosting techniques were not covered under ensembles. Overall, the course is awesome.


Mar 2, 2017

The course 3 got pretty technical pretty soon. Enjoyed the first 2 courses without feeling overwhelmed. But course 3 was challenging. I suppose building the expectation of what is to come can reduce the challenge and lead to faster and more number of course completions.

By Aleksander G

Apr 11, 2016

Just one comment about how the course could be improved: the assignments should be more hands-on with fewer pieces of code written in advance. I say this is even though I am not a skilled programmer. The assignments would be a bit harder, but also a bit more rewarding.

By Jaime A C B

Sep 12, 2016

Sometimes is difficult to understand the concept behind Classification because some videos are more practical than theorical, I mean it could be better to start the video explaining some concepts and then show and explan some samples and theorical issues.


By Nicolas S

Jan 2, 2020

The course itself is well structured and introduce gradually the complexity. Unfortunately, the exercises requires the use of a specific library, instead of scikit-learn and numpy. Furthermore, they also required Python 2, while Python 3 is now widely used.

By Martin B

Apr 11, 2019

As with all the courses in this specialization: great production values, excellent tuition. Useful assignments, even though the reliance of Graphlab Create is a bit of a drag. I also would have liked to see some discussion of Support Vector Machines.

By J N B P

Oct 9, 2020

This course covers all the core algorithms used in Classification models. If you have a basic understanding of machine learning, this course can help you build your understanding of classification on a deeper level.

By Uichong D L

Sep 17, 2017

Using discontinued Graphlab in the programming assignment is a minus and low activities in the forum makes hard to find assistance from the communities or mentors but the course material itself is just great.

By João S

Apr 18, 2016

Very good content, very well explained... great course. Classification its a very broad topic but i think this is great introduction.

The hands on where kinda on the easy side... but very interesting.

By David F

Aug 7, 2016

Not as good as the previous courses in this specialization - I agree with those who have noted that this one seemed a little rushed. However, these are still the best courses I've found on Coursera.

By Ahmed N

Feb 22, 2018

Great knowledge about machine learning fundamentals, More math illustration needed though it's great knowledge and very great basics about different machine learning algorithm used in reality

By Eric M

Apr 15, 2017

Extremely clear and informative. Good introduction to ML. I felt the labs could have had us write a little more of our own code, and would have been better to use non-proprietary libraries.

By Dawid L

Mar 20, 2017

Presented content is rather clear and instructors are rather easy to follow. Only the assignments are often confusing as there are questions which refer to missing content.

By Thuc D X

Jun 27, 2019

Sometimes the assignment description was hard to follow along. Overall, the course equips me a good understand and practical skills to tackle classification tasks.

By Gaurav K J

May 1, 2018

I learnt a lot, but I feel course 2 was very well made and this one felt a bit unstructured in comparison. Also, assignments in this course were made very easy.

By Justin K

Jun 10, 2016

Assignments were a little too easy, considering that students are expected to have taken the first two courses in the specialization. Otherwise, great course!

By Hao H

Jun 12, 2016

Good course overall. Some difficult materials such as boosting were not clear enough and I had to look into a few online resources to really understand it.

By 김대성

Mar 23, 2021

Very nice lecture & materials. The only slight negative component this lecture contains is the library used for the programming assignment.

By Fangzhe G

Feb 7, 2020

This course could be better if more programming content was taught. The programming assignments are difficult and not taught in courses.

By Brian B

Apr 22, 2016

Great course. I'm really looking forward to learn more about clustering in the next course since I know nearly nothing about clustering.