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

4.7
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

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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401 - 425 of 447 Reviews for Machine Learning: Classification

By VIGNESHKUMAR R

Aug 23, 2019

good

By Gareth J

Aug 26, 2019

A good course to teach the key points.

By Ashish C

Mar 31, 2019

more topics like deep learning, neural networks need to be introduced

By Neelkanth S M

Apr 08, 2019

The content is good but completing assignments is a real pain because they choose to deploy a unstable proprietary python library, which gives hard time installing and running (as of Q1 2019). The entire learning experience is marred by this Graphlab python library.

By Pier L L

Mar 26, 2017

Nice course but I would have expected more techniques (SVM for instance)

By Virgil P

Feb 18, 2018

The exercises/assignments are far too simple

By Tom L

Oct 21, 2016

Well, after the regression course, which I actually found interesting, the classification course doesn't look so good. The programming assignments are mostly pointless. The use of graphlab doesn't make it better. The info presented in this course is rather superficial. If you're entirely new to machine learning, you could find some value in this course. If not, go buy a good book.

By Ziyue Z

Aug 10, 2016

Compared with the regression course, this course was a slight disappointment. 1. there is less material compared to the regression course. Maybe this is because classification concepts are more intuitive. 2. the slides are much less prepared. Some of the sides even re-use earlier lesson slides in the beginning as a "review", much like soap operas re-use scenes from earlier episodes as "memory recall" to fill air time. 3. the math is more handwavy compared to the regression course. Neither course are supposed to go in depth with proofs, but I felt the regression course was at the right level and this course degraded too far. Do note it's very possible that I'm biased because I have seen more of the material from this course than the regression course.

By Fengchen G

May 19, 2016

The course content seemed to be rushed out, as a result, the quality is not as good as the first two.

By Ole H S

Jun 16, 2016

First. I like these courses allot. They are pretty close to covering just what you need to actually do machine learning in the real world and not dive too deep into topics that have no practical value.

However:

This course was a bit too thin, the last 4 weeks of the course contained little in depth informations and seemed to brush over allot of different topics that could have contained more information. Although they where important topics the course could go more in depth on at least 3 or 4 of those topics. The last 3 weeks could have been a course on its own if properly explored. However the concepts are well enough covered to be usable in practice i belive.

The programming exercises where ridiculously simple. Everything was reduced to filling in 1 or two lines in a bigger function. I understand that the point was to see how these functions are made and that it increases our understanding of the algorithms already existing in packages like schikit-learn and graphlab. Also the content became a bit too repetetive (actually started in the second course but continues in this course). The time used on variation over the same topic in different models made it challenging to pay attention when the lecture finally came to a new point (brain fell a sleep while waiting for something new).

By Matt T

Apr 12, 2016

Good, but overemphasizes niche software product (graphlab).

By Supharerk T

Jul 06, 2016

All of the courses lecture are great until it reaches week 5 where it's really hard to catch, the programming assignment doesn't give enough hints and lecture in this topic doesn't help much.

By Divya B

Jun 13, 2018

Pros: Absolutely fantastic theory explanations. Establishes solid fundamentals. Cons: The bugs in test/notebooks could have not been rectified with new ones. Demands searching in discussion forum every time. Would highly recommend for starters!

By Kumar B

Oct 04, 2017

This course covers the basics of classification very well, but I would have liked optional sections on more advanced topics. Some of the quiz questions were a bit confusing. It would have been good if the exercises also dealt with unbalanced data sets in more detail.

By Dmitri B

Jun 06, 2017

Theory Quizes are good, but programming assignment not so good for me.

By Ilan S

Nov 23, 2016

The videos were pretty goods. But a bit too slow and easy. The assigments were ok, but too guiding. Also there were too much reimplementation of algorithm

By Mounir

Jun 19, 2016

Exercises for Scikit-learn users were not organised.

Course took too long to start

By Nitsan O

Apr 25, 2016

The course is interesting and well taught. The professor is very enthusiastic and it makes the course fun to watch. The problem in my opinion is that the content is too superficial. It's completely lack of mathematical background and the programming exercises are sometimes no more than copy paste.

By nazar p

Jun 30, 2017

While courses 1 and 2 of this specialization were quite good, I find this one a bit sparse on content. I think this course could be easily compressed into 2-3 weeks instead of 7.

By 오승윤

Dec 04, 2016

Turi stopped working on SFrame (at least on Github), and SFrame does not supports Python 3. Expect some difficulty if you use other tools like pandas - the programming assignment completely assumes you use SFrame. Fortunately data of csv format is provided, so you can complete it anyway but again, don't expect a smooth ride.

Also the lecture tends to cover general concepts than mathematical details. I don't like it, but that would be a good point to the starters.

By Tu L P H

Jun 28, 2018

Why don't you guys talk about ID3 or CART algorithm at all? This one is too basic.

By Oliverio J S J

Jun 17, 2018

At first the course seems interesting but, as it progresses, it fails to convey why these contents are important in the deep learning era. In addition, it seems quite obvious that some contents are missing; I suppose that they have been eliminated due to the same problems that forced the cancellation of the last specialization courses.

By 陈弘毅

Feb 03, 2018

too simple

By Rohit J

May 12, 2016

A lot of interesting parts of the course are available as optional and a lot of the difficult parts of the coding exercises are provided to you - the challenge is not there. :/

By Omkar v D

Aug 14, 2018

.