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

4.7
2,896 ratings
481 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

SS

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!

CJ

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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376 - 400 of 449 Reviews for Machine Learning: Classification

By Tomasz J

Apr 04, 2016

Great course! However I put only 4 starts because I would like to see random forests which are not present.

By Thrivikrama

Oct 12, 2016

Good course.. Should have SVM related info too -- waiting for the promised optional videos from Prof. Carlos

By João F A d 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 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 Sander v d O

May 09, 2016

Simply a great course. Good intro to machine learning classifiation.

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 charan S

Jul 30, 2017

Very nice course, detailed explanations and visualizations.

By Bruno G E

Apr 17, 2016

Lack some of classical classification algorithms like SVM and Neural Netwroks.

By Antonio P L

Apr 30, 2016

Fantastic Course

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 MARIANA L J

Aug 12, 2016

The good:

-Good examples to learn the concepts

-Good organization of the material

-The assignments were well-explained and easy to follow-up

-The good humor and attitude of the professor makes the lectures very engaging

-All videolectures are small and this makes them easy to digest and follow (optional videos were large compared with the rest of the lectures but the material covered on those was pretty advanced and its length is justifiable)

Things that can be improved:

-In some of the videos the professor seemed to cruise through some of the concepts. I understand that it is recommended to take the series of courses in certain order but sometimes I felt we were rushing through the material covered

-I may be nitpicking here but I wish the professor used a different color to write on the slides (the red he used clashed horribly with some of the slides' backgrounds and made it difficult to read his observations)

Overall, a good course to take and very easy to follow if taken together with the other courses in the series.

By Ning A

Sep 16, 2016

Learn more than just classification, but also learn how to understand the ideas behind classification algorithms.

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.

Thanks.

By Vladislav V

May 13, 2016

It feels like it lacks certain depth.

By Gaurav K J

May 01, 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 Amilkar A H M

Nov 27, 2017

It's a great course, but the programming assignments are a little too guided. That is good, to some extend, as it allows you to focus on the concepts, but at the same time, it leave little space for actually practicing your coding skills. I know they said from the beginning that this course was not focused on the implementation of the algorithms, however, how are you going to be able to use what you've learned without knowing how to implement the algorithms on your own.

When it comes to coding, nothing replace implementing the algorithms yourself. That is my only complaint. Other than that, it's great. I loved it. The concepts were well explained and they covered a lot of material. I wish they had spent more time in certain topics, but I guess this is just an introduction. Anyway, take this course by any means if you have some programming experience and have little to no machine learning knowledge.

By Sahil M

Jul 10, 2018

Was a good course with some in-depth topics covered!

By mahesh

Aug 04, 2018

I can give a five star for this course, but removed one star cause graphlab api annoyed me a lot of times. The theory covered in this is course is good. The programming assignments are well structured but if api's like pandas, numpy, scikit learn were used it would have made my life easy.

By Farmer

Aug 12, 2018

Exercises are way too easy.

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 Fahad S

Nov 03, 2018

The content was excellent and the exercises were really good. It would be better if svms and bayesian classifiers are also covered

By Sacha v W

Nov 10, 2018

The course is well structured and very well explained. The structure is step by step increasing the the complexity. The programming exercises are excellent. I really appreciate the humor and passion of Carlos in teaching the material and his ability to explain complex matters with simple examples. The only drawback is that the course uses python packages that are less familiar. That is why I audited the course and worked with pandas and sklearn.

By Ragunandan R M

Sep 17, 2018

Good overall course.

By Srinivas C

Dec 02, 2018

This course was really good and helped in understanding different techniques in Classification

By Thuc D X

Jun 28, 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.